In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,...In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of tampering.Although deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational efficiency.To address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection techniques.In contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream.This design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level interaction.Furthermore,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region detection.Compared with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets.In addition,it has been extensively validated on other datasets,including CASIA and DIS25k.Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.展开更多
Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentica...Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies.The authors’difficulties were tampering detection,authentication,and integrity verification of the digital contents.This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the Internet.The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark information.The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely.Once the watermark is embedded with no damage to the original document,it is then shared with the destination.The watermark extraction process is performed to get the original document securely.The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different measures.The simulation values indicated that the ADMDTW-TAD technique improved performance over other models.展开更多
In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information ...In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.展开更多
In this paper,a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authenticati...In this paper,a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents.The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach(SAMMZWA).Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach.The SAMMZWA approach embeds and detects the watermark logically without altering the original text document.The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,SAMMZWA has been implemented and validated with attacked Arabic text.Experiments were performed on four datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results show that our method is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.展开更多
With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security ...With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.展开更多
Due to the rapid increase in the exchange of text information via internet networks,the security and the reliability of digital content have become a major research issue.The main challenges faced by researchers are a...Due to the rapid increase in the exchange of text information via internet networks,the security and the reliability of digital content have become a major research issue.The main challenges faced by researchers are authentication,integrity verication,and tampering detection of the digital contents.In this paper,text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents.The proposed approach embeds and detects the watermark logically without altering the original English text document.Based on hidden Markov model(HMM),the fourth level order of the word mechanism is used to analyze the contents of the given English text to nd the interrelationship between the contexts.The extracted features are used as watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,the proposed approach has been implemented and validated with attacked English text.Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion,reorder,and deletion attacks.The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks.Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.展开更多
Themost common digital media exchanged via the Internet is in text form.The Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a c...Themost common digital media exchanged via the Internet is in text form.The Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning.In this paper,an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet.The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques.The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model.The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text.The proposed scheme is implemented using PHP with VS code IDE.The simulation results,using varying sizes of standard datasets,show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks.Comparison results with other baseline techniques show the added value of the proposed scheme.展开更多
Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research issue.The main challenges faced by researchers are how ...Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research issue.The main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital text.In this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet network.Towards this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing approaches.In the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital text.This is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital watermark.This digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic text.An extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks.展开更多
In this paper,we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations.By estimatin...In this paper,we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations.By estimating the key parameters of the joint attack and compensating them in the identification algorithm,a compensation-oriented defense scheme is proposed.Then the identification algorithm of system parameter is designed and is further proved to be consistent.The asymptotic normality of the algorithm is obtained,and on this basis,we propose the optimal defense scheme.Furthermore,the implementation of the optimal defense scheme is discussed.Finally,a simulation example is presented to verify the effectiveness of the main results.展开更多
Tampering of biometric data has attracted a great deal of attention recently. Furthermore, there could be an intentional or accidental use of a particular biometric sample instead of another for a particular applicati...Tampering of biometric data has attracted a great deal of attention recently. Furthermore, there could be an intentional or accidental use of a particular biometric sample instead of another for a particular application. Therefore, there exists a need to propose a method to detect data tampering, as well as differentiate biometric samples in cases of intentional or accidental use for a different application. In this paper, fingerprint image tampering is studied. Furthermore, optically acquired fingerprints, synthetically generated fingerprints and contact-less acquired fingerprints are studied for separation purposes using the Benford’s law divergence metric. Benford’s law has shown in literature to be very effective in detecting tampering of natural images. In this paper, the Benford’s law features with support vector machine are proposed for the detection of malicious tampering of JPEG fingerprint images. This method is aimed at protecting against insider attackers and hackers. This proposed method detected tampering effectively, with Equal Error Rate (EER) of 2.08%. Again, the experimental results illustrate that, optically acquired fingerprints, synthetically generated fingerprints and contact-less acquired fingerprints can be separated by the proposed method effectively.展开更多
Blind forensics of JPEG image tampering as a kind of digital image blind forensics technology is gradually becoming a new research hotspot in the field of image security. Firstly, the main achievements of domestic and...Blind forensics of JPEG image tampering as a kind of digital image blind forensics technology is gradually becoming a new research hotspot in the field of image security. Firstly, the main achievements of domestic and foreign scholars in the blind forensic technology of JPEG image tampering were briefly described. Then, according to the different methods of tampering and detection, the current detection was divided into two types: double JPEG compression detection and block effect inconsistency detection. This paper summarized the existing methods of JPEG image blind forensics detection, and analyzed the two methods. Finally, the existing problems and future research trends were analyzed and prospected to provide further theoretical support for the research of JPEG image blind forensics technology.展开更多
Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
On the internet,image tampering has become awidespread issue,leading to a series of adverse effects on the trustworthiness of image information.In response to this challenge,this paper proposes an image tampering loca...On the internet,image tampering has become awidespread issue,leading to a series of adverse effects on the trustworthiness of image information.In response to this challenge,this paper proposes an image tampering localization method based on dual-stream feature fusion.Our approach employs a dualstream encoder to simultaneously extract features from both the RGB stream and the noise stream,enabling the localization of forged regions.By introducing an attention mechanism,these two feature streams are fused,further enhancing the detection performance.Additionally,the Atrous Spatial Pyramid Pooling(ASPP)module is integrated to expand the receptive field and extract contextual information at different scales.Finally,the decoder generates a tamper region localization map.Experimental results demonstrate that the proposed method exhibits significant performance improvements on three widely used datasets,affirming its effectiveness in the field of image tampering detection.展开更多
Aiming at the problems of current image tampering detectionmethods,such as inaccurate localization or poor robustness.We propose a novel network model structure leveraging a hybrid attentionmechanism.The model incorpo...Aiming at the problems of current image tampering detectionmethods,such as inaccurate localization or poor robustness.We propose a novel network model structure leveraging a hybrid attentionmechanism.The model incorporates two parallel branches:the main branch is dedicated to extracting features from RGB images,emphasizing the identification of visual artifacts like unnatural tampering boundaries and strong contrast differences,while the secondary branch,employing constrained convolution and the spatial rich model(SRM)filter,is focused on extracting features associated with noise.To enhance image representation,we introduce a hybrid attention mechanism module within the dual stream.This module includes a positional attention mechanism and a window-based selfattention mechanism.Additionally,we employ atrous spatial pyramid pooling to effectively fuse the features from the dual streams.The experimental results showcase the efficiency of the suggested approach,outclassing several advanced techniques in both detection and localization assignments.展开更多
Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited...Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.展开更多
Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing...Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools.The manual forgery localization is often reliant on forensic expertise.In recent times,machine learning(ML)and deep learning(DL)have shown promising results in automating image forgery localization.However,the ML-based method relies on hand-crafted features.Conversely,the DL method automatically extracts shallow spatial features to enhance the accuracy.However,DL-based methods lack the global co-relation of the features due to this performance degradation noticed in several applications.In the proposed study,we designed FLTNet(forgery localization transformer network)with a CNN(convolution neural network)encoder and transformer-based attention.The encoder extracts local high-dimensional features,and the transformer provides the global co-relation of the features.In the decoder,we have exclusively utilized a CNN to upsample the features that generate tampered mask images.Moreover,we evaluated visual and quantitative performance on three standard datasets and comparison with six state-of-the-art methods.The IoU values of the proposed method on CASIA V1,CASIA V2,and CoMoFoD datasets are 0.77,0.82,and 0.84,respectively.In addition,the F1-scores of these three datasets are 0.80,0.84,and 0.86,respectively.Furthermore,the visual results of the proposed method are clean and contain rich information,which can be used for real-time forgery detection.The code used in the study can be accessed through URL:https://github.com/ajit2k5/Forgery-Localization(accessed on 21 January 2025).展开更多
Many real world attacks often target the implementation of a cryptographic scheme,rather than the algorithm itself,and a system designer has to consider new models that can capture these attacks.For example,if the key...Many real world attacks often target the implementation of a cryptographic scheme,rather than the algorithm itself,and a system designer has to consider new models that can capture these attacks.For example,if the key can be tampered by physical attacks on the device,the security of the scheme becomes totally unclear.In this work,we investigate predicate encryption(PE),a powerful encryption primitive,in the setting of tampering attacks.First,we show that many existing frameworks to construct PE are vulnerable to tampering attacks.Then we present a new security notion to capture such attacks.Finally,we take Attrapadung’s framework in Eurocrypt’14 as an example to show how to"compile"these frameworks to tampering resilient ones.Moreover,our method is compatible with the original pair encoding schemes without introducing any redundancy.展开更多
Many real world attacks often target the implementation of a cryptographic scheme,rather than the algorithm itself,and a system designer has to consider new models that can capture these attacks.For example,if the key...Many real world attacks often target the implementation of a cryptographic scheme,rather than the algorithm itself,and a system designer has to consider new models that can capture these attacks.For example,if the key can be tampered by physical attacks on the device,the security of the scheme becomes totally unclear.In this work,we investigate predicate encryption(PE),a powerful encryption primitive,in the setting of tampering attacks.First,we show that many existing frameworks to construct PE are vulnerable to tampering attacks.Then we present a new security notion to capture such attacks.Finally,we take Attrapadung’s framework in Eurocrypt’14 as an example to show how to“compile"these frameworks to tampering resilient ones.Moreover,our method is compatible with the original pair encoding schemes without introducing any redundancy.展开更多
Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorp...Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.展开更多
As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social stability.In the realm of image tampering localization,accurately l...As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social stability.In the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of challenges.These issues impede the model’s universality and generalization capability and detrimentally affect its performance.To tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization.Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream integration.Meanwhile,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization performance.To comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature fusion.This facilitates a more precise localization of tampered regions of various sizes.Furthermore,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered images.This strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy loss.As a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is enhanced.Experimental evaluations on five benchmark datasets(IMD-20,CASIA,NIST-16,Columbia and Coverage)validate the effectiveness of our proposed model.The meticulously calibrated FL-MobileViT model consistently outperforms numerous existing general models regarding localization accuracy across diverse datasets,demonstrating superior adaptability.展开更多
基金supported by National Natural Science Foundation of China(No.61502274).
文摘In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of tampering.Although deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational efficiency.To address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection techniques.In contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream.This design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level interaction.Furthermore,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region detection.Compared with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets.In addition,it has been extensively validated on other datasets,including CASIA and DIS25k.Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Research Groups Program Grant No.(RGP-1443-0051).
文摘Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies.The authors’difficulties were tampering detection,authentication,and integrity verification of the digital contents.This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the Internet.The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark information.The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely.Once the watermark is embedded with no damage to the original document,it is then shared with the destination.The watermark extraction process is performed to get the original document securely.The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different measures.The simulation values indicated that the ADMDTW-TAD technique improved performance over other models.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60971095 and No.61172109)Artificial Intelligence Key Laboratory of Sichuan Province(Grant No.2012RZJ01)the Fundamental Research Funds for the Central Universities(Grant No.DUT13RC201)
文摘In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.
基金the Deanship of Scientific Research at King Khalid University for funding this work under grant number(R.G.P.2/55/40/2019),Received by Fahd N.Al-Wesabi.www.kku.edu.sa。
文摘In this paper,a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents.The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach(SAMMZWA).Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach.The SAMMZWA approach embeds and detects the watermark logically without altering the original text document.The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,SAMMZWA has been implemented and validated with attacked Arabic text.Experiments were performed on four datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results show that our method is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.
文摘With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.
基金The author extends his appreciation to the Deanship of Scientic Research at King Khalid University for funding this work under grant number(R.G.P.2/55/40/2019),Received by Fahd N.Al-Wesabi.www.kku.edu.sa.
文摘Due to the rapid increase in the exchange of text information via internet networks,the security and the reliability of digital content have become a major research issue.The main challenges faced by researchers are authentication,integrity verication,and tampering detection of the digital contents.In this paper,text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents.The proposed approach embeds and detects the watermark logically without altering the original English text document.Based on hidden Markov model(HMM),the fourth level order of the word mechanism is used to analyze the contents of the given English text to nd the interrelationship between the contexts.The extracted features are used as watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,the proposed approach has been implemented and validated with attacked English text.Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion,reorder,and deletion attacks.The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks.Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(GRP/14/42),Received by Fahd N.Al-Wesabi.www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Themost common digital media exchanged via the Internet is in text form.The Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning.In this paper,an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet.The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques.The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model.The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text.The proposed scheme is implemented using PHP with VS code IDE.The simulation results,using varying sizes of standard datasets,show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks.Comparison results with other baseline techniques show the added value of the proposed scheme.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP.1/53/42),Received by Mohammed Alamgeer.www.kku.edu.sa。
文摘Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research issue.The main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital text.In this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet network.Towards this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing approaches.In the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital text.This is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital watermark.This digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic text.An extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks.
文摘In this paper,we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations.By estimating the key parameters of the joint attack and compensating them in the identification algorithm,a compensation-oriented defense scheme is proposed.Then the identification algorithm of system parameter is designed and is further proved to be consistent.The asymptotic normality of the algorithm is obtained,and on this basis,we propose the optimal defense scheme.Furthermore,the implementation of the optimal defense scheme is discussed.Finally,a simulation example is presented to verify the effectiveness of the main results.
文摘Tampering of biometric data has attracted a great deal of attention recently. Furthermore, there could be an intentional or accidental use of a particular biometric sample instead of another for a particular application. Therefore, there exists a need to propose a method to detect data tampering, as well as differentiate biometric samples in cases of intentional or accidental use for a different application. In this paper, fingerprint image tampering is studied. Furthermore, optically acquired fingerprints, synthetically generated fingerprints and contact-less acquired fingerprints are studied for separation purposes using the Benford’s law divergence metric. Benford’s law has shown in literature to be very effective in detecting tampering of natural images. In this paper, the Benford’s law features with support vector machine are proposed for the detection of malicious tampering of JPEG fingerprint images. This method is aimed at protecting against insider attackers and hackers. This proposed method detected tampering effectively, with Equal Error Rate (EER) of 2.08%. Again, the experimental results illustrate that, optically acquired fingerprints, synthetically generated fingerprints and contact-less acquired fingerprints can be separated by the proposed method effectively.
文摘Blind forensics of JPEG image tampering as a kind of digital image blind forensics technology is gradually becoming a new research hotspot in the field of image security. Firstly, the main achievements of domestic and foreign scholars in the blind forensic technology of JPEG image tampering were briefly described. Then, according to the different methods of tampering and detection, the current detection was divided into two types: double JPEG compression detection and block effect inconsistency detection. This paper summarized the existing methods of JPEG image blind forensics detection, and analyzed the two methods. Finally, the existing problems and future research trends were analyzed and prospected to provide further theoretical support for the research of JPEG image blind forensics technology.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
文摘On the internet,image tampering has become awidespread issue,leading to a series of adverse effects on the trustworthiness of image information.In response to this challenge,this paper proposes an image tampering localization method based on dual-stream feature fusion.Our approach employs a dualstream encoder to simultaneously extract features from both the RGB stream and the noise stream,enabling the localization of forged regions.By introducing an attention mechanism,these two feature streams are fused,further enhancing the detection performance.Additionally,the Atrous Spatial Pyramid Pooling(ASPP)module is integrated to expand the receptive field and extract contextual information at different scales.Finally,the decoder generates a tamper region localization map.Experimental results demonstrate that the proposed method exhibits significant performance improvements on three widely used datasets,affirming its effectiveness in the field of image tampering detection.
基金The National Natural Science Foundation of China(61602295).
文摘Aiming at the problems of current image tampering detectionmethods,such as inaccurate localization or poor robustness.We propose a novel network model structure leveraging a hybrid attentionmechanism.The model incorporates two parallel branches:the main branch is dedicated to extracting features from RGB images,emphasizing the identification of visual artifacts like unnatural tampering boundaries and strong contrast differences,while the secondary branch,employing constrained convolution and the spatial rich model(SRM)filter,is focused on extracting features associated with noise.To enhance image representation,we introduce a hybrid attention mechanism module within the dual stream.This module includes a positional attention mechanism and a window-based selfattention mechanism.Additionally,we employ atrous spatial pyramid pooling to effectively fuse the features from the dual streams.The experimental results showcase the efficiency of the suggested approach,outclassing several advanced techniques in both detection and localization assignments.
基金the National Natural Science Foundation of China(Grant No.62172132)Public Welfare Technology Research Project of Zhejiang Province(Grant No.LGF21F020014)the Opening Project of Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security of Zhejiang Police College(Grant No.2021DSJSYS002).
文摘Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.
文摘Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools.The manual forgery localization is often reliant on forensic expertise.In recent times,machine learning(ML)and deep learning(DL)have shown promising results in automating image forgery localization.However,the ML-based method relies on hand-crafted features.Conversely,the DL method automatically extracts shallow spatial features to enhance the accuracy.However,DL-based methods lack the global co-relation of the features due to this performance degradation noticed in several applications.In the proposed study,we designed FLTNet(forgery localization transformer network)with a CNN(convolution neural network)encoder and transformer-based attention.The encoder extracts local high-dimensional features,and the transformer provides the global co-relation of the features.In the decoder,we have exclusively utilized a CNN to upsample the features that generate tampered mask images.Moreover,we evaluated visual and quantitative performance on three standard datasets and comparison with six state-of-the-art methods.The IoU values of the proposed method on CASIA V1,CASIA V2,and CoMoFoD datasets are 0.77,0.82,and 0.84,respectively.In addition,the F1-scores of these three datasets are 0.80,0.84,and 0.86,respectively.Furthermore,the visual results of the proposed method are clean and contain rich information,which can be used for real-time forgery detection.The code used in the study can be accessed through URL:https://github.com/ajit2k5/Forgery-Localization(accessed on 21 January 2025).
基金This work was supported in part by National Natural Science Foundation of China(No.61632020,61472416,61772520)National key research and development program of China(No.2017YFB0802705)+1 种基金Key Research Project of Zhejiang Province(No.2017C01062)Fundamental Theory and Cutting-edge Technology Research Program of Institute of Information Engineering,CAS(No.Y7Z0321102).
文摘Many real world attacks often target the implementation of a cryptographic scheme,rather than the algorithm itself,and a system designer has to consider new models that can capture these attacks.For example,if the key can be tampered by physical attacks on the device,the security of the scheme becomes totally unclear.In this work,we investigate predicate encryption(PE),a powerful encryption primitive,in the setting of tampering attacks.First,we show that many existing frameworks to construct PE are vulnerable to tampering attacks.Then we present a new security notion to capture such attacks.Finally,we take Attrapadung’s framework in Eurocrypt’14 as an example to show how to"compile"these frameworks to tampering resilient ones.Moreover,our method is compatible with the original pair encoding schemes without introducing any redundancy.
基金supported in part by National Natural Science Foundation of China(No.61632020,61472416,61772520)National key research and development program of China(No.2017YFB0802705)+1 种基金Key Research Project of Zhejiang Province(No.2017C01062)Fundamental Theory and Cutting-edge Technology Research Program of Institute of Information Engineering,CAS(No.Y7Z0321102).
文摘Many real world attacks often target the implementation of a cryptographic scheme,rather than the algorithm itself,and a system designer has to consider new models that can capture these attacks.For example,if the key can be tampered by physical attacks on the device,the security of the scheme becomes totally unclear.In this work,we investigate predicate encryption(PE),a powerful encryption primitive,in the setting of tampering attacks.First,we show that many existing frameworks to construct PE are vulnerable to tampering attacks.Then we present a new security notion to capture such attacks.Finally,we take Attrapadung’s framework in Eurocrypt’14 as an example to show how to“compile"these frameworks to tampering resilient ones.Moreover,our method is compatible with the original pair encoding schemes without introducing any redundancy.
文摘Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.
基金This study was funded by the Science and Technology Project in Xi’an(No.22GXFW0123)this work was supported by the Special Fund Construction Project of Key Disciplines in Ordinary Colleges and Universities in Shaanxi Province,the authors would like to thank the anonymous reviewers for their helpful comments and suggestions.
文摘As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social stability.In the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of challenges.These issues impede the model’s universality and generalization capability and detrimentally affect its performance.To tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization.Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream integration.Meanwhile,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization performance.To comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature fusion.This facilitates a more precise localization of tampered regions of various sizes.Furthermore,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered images.This strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy loss.As a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is enhanced.Experimental evaluations on five benchmark datasets(IMD-20,CASIA,NIST-16,Columbia and Coverage)validate the effectiveness of our proposed model.The meticulously calibrated FL-MobileViT model consistently outperforms numerous existing general models regarding localization accuracy across diverse datasets,demonstrating superior adaptability.
