Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest ...Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest and buying potentials resulted in a number of forgeries manufactured and introduced to the open antique market.The items such as early silver candlesticks dictate a very high price,for many high quality fakes show very good appearances and matching similarities with originals.Such copies are traditionally manufactured by casting using the original items as patterns.Small details and variances in design features,position and shape of hallmarks,including the final surface quality are usual features to distinguish the fakes from the originals.This paper presents results of a study conducted on several silver candlesticks,including two artefacts bearing features of those produced in the mid 18th century,one original Italian candelabrum from Fascist era,and small candlesticks made in the early 20th century.Also,the paper presents some interesting contemporary coins-replicas of many those produced in different countries.The coins were offered for sale by unscrupulous dealers via auctions and e-bays.Finally the main results and findings from this study are discussed from a manufacturing point of view,such as fabrication technology,surface quality and hallmarks,which will help the collectors,dealers and investors to detect and avoid forgeries.展开更多
Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. There...Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.展开更多
With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential misuse.Deepfakes pose a significant threat to society,partic-ularl...With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential misuse.Deepfakes pose a significant threat to society,partic-ularly by infringing on individuals’privacy.Amid significant endeavors to fabricate systems for identifying deepfake fabrications,existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations,thereby hindering their broad applicability to images and videos produced by unfamiliar technologies.In this manuscript,we endorse resilient training tactics to amplify generalization capabilities.In adversarial training,models are trained using deliberately crafted samples to deceive classification systems,thereby significantly enhancing their generalization ability.In response to this challenge,we propose an innovative hybrid adversarial training framework integrating Virtual Adversarial Training(VAT)with Two-Generated Blurred Adversarial Training.This combined framework bolsters the model’s resilience in detecting deepfakes made using unfamiliar deep learning technologies.Through such adversarial training,models are prompted to acquire more versatile attributes.Through experimental studies,we demonstrate that our model achieves higher accuracy than existing models.展开更多
As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many de...As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many deep learning-based facial forgery detection approaches show promise,they often fail to delve deeply into the complex relationships between image features and forgery indicators,limiting their effectiveness to specific forgery techniques.To address this challenge,we propose a dual-branch collaborative deepfake detection network.The network processes video frame images as input,where a specialized noise extraction module initially extracts the noise feature maps.Subsequently,the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues.An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales.This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques.Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video.Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance,accuracy,and generalization ability.展开更多
In today’s digital era,the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation.Unfortunately,this progress has also given rise to the misuse of manipulat...In today’s digital era,the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation.Unfortunately,this progress has also given rise to the misuse of manipulated images across various domains.One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images.This paper offers a comprehensive survey of existing methodologies for detecting image tampering,shedding light on the diverse approaches employed in the field of contemporary image forensics.The methods used to identify image forgery can be broadly classified into two primary categories:classical machine learning techniques,heavily reliant on manually crafted features,and deep learning methods.Additionally,this paper explores recent developments in image forensics,placing particular emphasis on the detection of counterfeit colorization.Image colorization involves predicting colors for grayscale images,thereby enhancing their visual appeal.The advancements in colorization techniques have reached a level where distinguishing between authentic and forged images with the naked eye has become an exceptionally challenging task.This paper serves as an in-depth exploration of the intricacies of image forensics in the modern age,with a specific focus on the detection of colorization forgery,presenting a comprehensive overview of methodologies in this critical field.展开更多
In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by ...In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.展开更多
The exact dating of Laozi and his work has long been a topic of scholarly interest.Since the 1920s,traditional views on Laozi’s dating have been widely questioned in both Chinese and Western academia.In the latter ha...The exact dating of Laozi and his work has long been a topic of scholarly interest.Since the 1920s,traditional views on Laozi’s dating have been widely questioned in both Chinese and Western academia.In the latter half of the 20th century,as the“Trust Antiquity”trend gradually emerged in Chinese academia,the view that“Laozi did not exist”became the most influential mainstream perspective in Western academia.This paper first reviews the process of unification and differentiation of opinions between Chinese and Western academia.Then,by analyzing and comparing representative papers from Chinese and Western scholars,it explores the reasons for the differences in mainstream opinions.Additionally,it briefly discusses the implications of these differences to provide insights for future research.展开更多
The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading mar...The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.展开更多
The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,whic...The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.展开更多
Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for ...Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.展开更多
In recent years,visual facial forgery has reached a level of sophistication that humans cannot identify fraud,which poses a significant threat to information security.A wide range of malicious applications have emerge...In recent years,visual facial forgery has reached a level of sophistication that humans cannot identify fraud,which poses a significant threat to information security.A wide range of malicious applications have emerged,such as deepfake,fake news,defamation or blackmailing of celebrities,impersonation of politicians in political warfare,and the spreading of rumours to attract views.As a result,a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend.However,there is no comprehensive,fair,and unified performance evaluation to enlighten the community on best performing methods.The authors present a systematic benchmark beyond traditional surveys that provides in-depth insights into facial forgery and facial forensics,grounding on robustness tests such as contrast,brightness,noise,resolution,missing information,and compression.The authors also provide a practical guideline of the benchmarking results,to determine the characteristics of the methods that serve as a comparative reference in this never-ending war between measures and countermeasures.The authors’source code is open to the public.展开更多
Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while ...Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while in forgeries the consistency will be destroyed. We first extract the consistency of correlation coefficients of gray values (CCCoGV for short) after normalization and quantization as distinguishing feature to identify interframe forgeries. Then we test the CCCoGV in a large database with the help of SVM (Support Vector Machine). Experimental results show that the proposed method is efficient in classifying original videos and forgeries. Furthermore, the proposed method performs also pretty well in classifying frame insertion and frame deletion forgeries.展开更多
Some case studies are presented ranging from geological fakes and frauds,homicides and one environmental forensic case.Fakes may be true geological materials such as created fossils or gems and precious stones,or wher...Some case studies are presented ranging from geological fakes and frauds,homicides and one environmental forensic case.Fakes may be true geological materials such as created fossils or gems and precious stones,or where geological methods are used to analyse fakes,such as the stones or ceramics used in making archaeological or art forgeries(e.g.,mineral pigments in paintings).Fakes have also been created for reasons of academic rivalry,career advancement and religious belief.Fraud commonly involves over-stated claims of ore content associated with mining and the oil and gas industry.The range of geological fakes,the uses of geological methods in detecting fakes,and the extent of fraud in the mining sector are all extensive and sometimes incredible.The homicide is case presented to demonstrate how the types of geological investigation described in the rest of this volume may be applied.We include an environmental forensic case for similar reasons,to show that forensic geology may be applied to more than homicides and fakery.展开更多
In this study, the author has designed new verifiable (t, n) threshold untraceable signature schemes. The proposed schemes have the following properties: ( 1 ) Verification: The shadows of the secret distributed by th...In this study, the author has designed new verifiable (t, n) threshold untraceable signature schemes. The proposed schemes have the following properties: ( 1 ) Verification: The shadows of the secret distributed by the trusted center can be verified by all of the participants; (2) Security: Even if the number of the dishonest member is over the value of the threshold, they cannot get the system secret parameters , such as the group secret key, and forge other member's individual signature; (3) Efficient verification: The verifier can verify the group signature easily and the verification time of the group signature is equivalent to that of an individual signature; (4) Untraceability: The signers of the group signature cannot be traced.展开更多
Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost,they cannot identif...Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost,they cannot identify the flat duplicated regions without reliable extracted features. In this paper, we propose a new CMFD method by using speeded-up robust feature(SURF)in the opponent color space. Our method starts by converting the inspected image from RGB to the opponent color space. The color gradient per pixel is calculated and taken as the work space for SURF to extract the keypoints. The matched keypoints are clustered and their geometric transformations are estimated. Finally, the false matches are removed. Experimental results show that the proposed technique can effectively expose the duplicated regions with various transformations, even when the duplication regions are flat.展开更多
An effective algorithm is proposed to detect copy-move forgery.In this algorithm,first,the PatchMatch algorithm is improved by using a reliable order-statistics-based approximate nearest neighbor search algorithm(ROSA...An effective algorithm is proposed to detect copy-move forgery.In this algorithm,first,the PatchMatch algorithm is improved by using a reliable order-statistics-based approximate nearest neighbor search algorithm(ROSANNA)to modify the propagation process.Then,fractional quaternion Zernike moments(FrQZMs)are considered to be features extracted from color forged images.Finally,the extracted FrQZMs features are matched by the improved PatchMatch algorithm.The experimental results on two publicly available datasets(FAU and GRIP datasets)show that the proposed algorithm performs better than the state-of-the-art algorithms not only in objective criteria F-measure value but also in visual.Moreover,the proposed algorithm is robust to some attacks,such as additive white Gaussian noise,JPEG compression,rotation,and scaling.展开更多
Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm ...Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region.展开更多
Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approach...Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.展开更多
Kang, et al. [Journal of Electronics(China), 23(2006)4] proposed a threshold multi-proxy multi-signature scheme, and claimed the scheme satisfies the security requirements of threshold multi-proxy multi-signature. How...Kang, et al. [Journal of Electronics(China), 23(2006)4] proposed a threshold multi-proxy multi-signature scheme, and claimed the scheme satisfies the security requirements of threshold multi-proxy multi-signature. However, in this paper, two forgery attacks are proposed to show that their schemes have serious security flaws. To overcome theses flaws, an improvement on Kang, et al.’s scheme is proposed.展开更多
This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data...This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data communicated over networks. Hence, there is an urgent need fordeveloping efficient image forgery detection algorithms. Two main types offorgery are considered in this paper: splicing and copy-move. Splicing isperformed by inserting a part of an image into another image. On the otherhand, copy-move forgery is performed by copying a part of the image intoanother position in the same image. The proposed approach for splicingdetection is based on the assumption that illumination between the originaland tampered images is different. To detect the difference between the originaland tampered images, the homomorphic transform separates the illuminationcomponent from the reflectance component. The illumination histogramderivative is used for detecting the difference in illumination, and henceforgery detection is accomplished. Prior to performing the forgery detectionprocess, some pre-processing techniques, including histogram equalization,histogram matching, high-pass filtering, homomorphic enhancement, andsingle image super-resolution, are introduced to reinforce the details andchanges between the original and embedded sections. The proposed approachfor copy-move forgery detection is performed with the Speeded Up RobustFeatures (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matchingwith Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogramequalization and single-mage super-resolution. Simulation results proved thefeasibility and the robustness of the pre-processing step in homomorphicdetection and SURF detection algorithms for splicing and copy-move forgerydetection, respectively.展开更多
文摘Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest and buying potentials resulted in a number of forgeries manufactured and introduced to the open antique market.The items such as early silver candlesticks dictate a very high price,for many high quality fakes show very good appearances and matching similarities with originals.Such copies are traditionally manufactured by casting using the original items as patterns.Small details and variances in design features,position and shape of hallmarks,including the final surface quality are usual features to distinguish the fakes from the originals.This paper presents results of a study conducted on several silver candlesticks,including two artefacts bearing features of those produced in the mid 18th century,one original Italian candelabrum from Fascist era,and small candlesticks made in the early 20th century.Also,the paper presents some interesting contemporary coins-replicas of many those produced in different countries.The coins were offered for sale by unscrupulous dealers via auctions and e-bays.Finally the main results and findings from this study are discussed from a manufacturing point of view,such as fabrication technology,surface quality and hallmarks,which will help the collectors,dealers and investors to detect and avoid forgeries.
文摘Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSP2025R493。
文摘With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential misuse.Deepfakes pose a significant threat to society,partic-ularly by infringing on individuals’privacy.Amid significant endeavors to fabricate systems for identifying deepfake fabrications,existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations,thereby hindering their broad applicability to images and videos produced by unfamiliar technologies.In this manuscript,we endorse resilient training tactics to amplify generalization capabilities.In adversarial training,models are trained using deliberately crafted samples to deceive classification systems,thereby significantly enhancing their generalization ability.In response to this challenge,we propose an innovative hybrid adversarial training framework integrating Virtual Adversarial Training(VAT)with Two-Generated Blurred Adversarial Training.This combined framework bolsters the model’s resilience in detecting deepfakes made using unfamiliar deep learning technologies.Through such adversarial training,models are prompted to acquire more versatile attributes.Through experimental studies,we demonstrate that our model achieves higher accuracy than existing models.
基金funded by the Ministry of Public Security Science and Technology Program Project(No.2023LL35)the Key Laboratory of Smart Policing and National Security Risk Governance,Sichuan Province(No.ZHZZZD2302).
文摘As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many deep learning-based facial forgery detection approaches show promise,they often fail to delve deeply into the complex relationships between image features and forgery indicators,limiting their effectiveness to specific forgery techniques.To address this challenge,we propose a dual-branch collaborative deepfake detection network.The network processes video frame images as input,where a specialized noise extraction module initially extracts the noise feature maps.Subsequently,the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues.An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales.This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques.Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video.Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance,accuracy,and generalization ability.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A3049788).
文摘In today’s digital era,the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation.Unfortunately,this progress has also given rise to the misuse of manipulated images across various domains.One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images.This paper offers a comprehensive survey of existing methodologies for detecting image tampering,shedding light on the diverse approaches employed in the field of contemporary image forensics.The methods used to identify image forgery can be broadly classified into two primary categories:classical machine learning techniques,heavily reliant on manually crafted features,and deep learning methods.Additionally,this paper explores recent developments in image forensics,placing particular emphasis on the detection of counterfeit colorization.Image colorization involves predicting colors for grayscale images,thereby enhancing their visual appeal.The advancements in colorization techniques have reached a level where distinguishing between authentic and forged images with the naked eye has become an exceptionally challenging task.This paper serves as an in-depth exploration of the intricacies of image forensics in the modern age,with a specific focus on the detection of colorization forgery,presenting a comprehensive overview of methodologies in this critical field.
文摘In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.
文摘The exact dating of Laozi and his work has long been a topic of scholarly interest.Since the 1920s,traditional views on Laozi’s dating have been widely questioned in both Chinese and Western academia.In the latter half of the 20th century,as the“Trust Antiquity”trend gradually emerged in Chinese academia,the view that“Laozi did not exist”became the most influential mainstream perspective in Western academia.This paper first reviews the process of unification and differentiation of opinions between Chinese and Western academia.Then,by analyzing and comparing representative papers from Chinese and Western scholars,it explores the reasons for the differences in mainstream opinions.Additionally,it briefly discusses the implications of these differences to provide insights for future research.
基金the National Fund Project No.62172337National Natural Science Foundation of China(No.61662069)China Postdoctoral Science Foundation(No.2017M610817).
文摘The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.
文摘The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.
文摘Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.
基金QuỹĐổi mới sáng tạo Vingroup,Grant/Award Number:VINIF.2020.ThS.BK.10。
文摘In recent years,visual facial forgery has reached a level of sophistication that humans cannot identify fraud,which poses a significant threat to information security.A wide range of malicious applications have emerged,such as deepfake,fake news,defamation or blackmailing of celebrities,impersonation of politicians in political warfare,and the spreading of rumours to attract views.As a result,a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend.However,there is no comprehensive,fair,and unified performance evaluation to enlighten the community on best performing methods.The authors present a systematic benchmark beyond traditional surveys that provides in-depth insights into facial forgery and facial forensics,grounding on robustness tests such as contrast,brightness,noise,resolution,missing information,and compression.The authors also provide a practical guideline of the benchmarking results,to determine the characteristics of the methods that serve as a comparative reference in this never-ending war between measures and countermeasures.The authors’source code is open to the public.
文摘Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while in forgeries the consistency will be destroyed. We first extract the consistency of correlation coefficients of gray values (CCCoGV for short) after normalization and quantization as distinguishing feature to identify interframe forgeries. Then we test the CCCoGV in a large database with the help of SVM (Support Vector Machine). Experimental results show that the proposed method is efficient in classifying original videos and forgeries. Furthermore, the proposed method performs also pretty well in classifying frame insertion and frame deletion forgeries.
文摘Some case studies are presented ranging from geological fakes and frauds,homicides and one environmental forensic case.Fakes may be true geological materials such as created fossils or gems and precious stones,or where geological methods are used to analyse fakes,such as the stones or ceramics used in making archaeological or art forgeries(e.g.,mineral pigments in paintings).Fakes have also been created for reasons of academic rivalry,career advancement and religious belief.Fraud commonly involves over-stated claims of ore content associated with mining and the oil and gas industry.The range of geological fakes,the uses of geological methods in detecting fakes,and the extent of fraud in the mining sector are all extensive and sometimes incredible.The homicide is case presented to demonstrate how the types of geological investigation described in the rest of this volume may be applied.We include an environmental forensic case for similar reasons,to show that forensic geology may be applied to more than homicides and fakery.
文摘In this study, the author has designed new verifiable (t, n) threshold untraceable signature schemes. The proposed schemes have the following properties: ( 1 ) Verification: The shadows of the secret distributed by the trusted center can be verified by all of the participants; (2) Security: Even if the number of the dishonest member is over the value of the threshold, they cannot get the system secret parameters , such as the group secret key, and forge other member's individual signature; (3) Efficient verification: The verifier can verify the group signature easily and the verification time of the group signature is equivalent to that of an individual signature; (4) Untraceability: The signers of the group signature cannot be traced.
基金Supported by the Natural Science Foundation of Tianjin(No.15JCYBJC15500)
文摘Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost,they cannot identify the flat duplicated regions without reliable extracted features. In this paper, we propose a new CMFD method by using speeded-up robust feature(SURF)in the opponent color space. Our method starts by converting the inspected image from RGB to the opponent color space. The color gradient per pixel is calculated and taken as the work space for SURF to extract the keypoints. The matched keypoints are clustered and their geometric transformations are estimated. Finally, the false matches are removed. Experimental results show that the proposed technique can effectively expose the duplicated regions with various transformations, even when the duplication regions are flat.
基金The National Natural Science of China(No.61572258,61771231,61772281,61672294)the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe Qing Lan Project of Jiangsu Higher Education Institutions
文摘An effective algorithm is proposed to detect copy-move forgery.In this algorithm,first,the PatchMatch algorithm is improved by using a reliable order-statistics-based approximate nearest neighbor search algorithm(ROSANNA)to modify the propagation process.Then,fractional quaternion Zernike moments(FrQZMs)are considered to be features extracted from color forged images.Finally,the extracted FrQZMs features are matched by the improved PatchMatch algorithm.The experimental results on two publicly available datasets(FAU and GRIP datasets)show that the proposed algorithm performs better than the state-of-the-art algorithms not only in objective criteria F-measure value but also in visual.Moreover,the proposed algorithm is robust to some attacks,such as additive white Gaussian noise,JPEG compression,rotation,and scaling.
基金Supported by the National Natural Science Foundation of China(61472429,61070192,91018008,61303074,61170240)the National High Technology Research Development Program of China(863 Program)(2007AA01Z414)+1 种基金the National Science and Technology Major Project of China(2012ZX01039-004)the Beijing Natural Science Foundation(4122041)
文摘Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region.
基金supported in part by the National Natural Science Foundation of China under grant No.(61472429,61070192,91018008,61303074,61170240)Beijing Natural Science Foundation under grant No.4122041+1 种基金National High-Tech Research Development Program of China under grant No.2007AA01Z414National Science and Technology Major Project of China under grant No.2012ZX01039-004
文摘Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.
基金Supported by the National Natural Science Foundation of China (No.60503005)the Natural Science Foundation of Hunan Province (No.07JJ6110)
文摘Kang, et al. [Journal of Electronics(China), 23(2006)4] proposed a threshold multi-proxy multi-signature scheme, and claimed the scheme satisfies the security requirements of threshold multi-proxy multi-signature. However, in this paper, two forgery attacks are proposed to show that their schemes have serious security flaws. To overcome theses flaws, an improvement on Kang, et al.’s scheme is proposed.
文摘This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data communicated over networks. Hence, there is an urgent need fordeveloping efficient image forgery detection algorithms. Two main types offorgery are considered in this paper: splicing and copy-move. Splicing isperformed by inserting a part of an image into another image. On the otherhand, copy-move forgery is performed by copying a part of the image intoanother position in the same image. The proposed approach for splicingdetection is based on the assumption that illumination between the originaland tampered images is different. To detect the difference between the originaland tampered images, the homomorphic transform separates the illuminationcomponent from the reflectance component. The illumination histogramderivative is used for detecting the difference in illumination, and henceforgery detection is accomplished. Prior to performing the forgery detectionprocess, some pre-processing techniques, including histogram equalization,histogram matching, high-pass filtering, homomorphic enhancement, andsingle image super-resolution, are introduced to reinforce the details andchanges between the original and embedded sections. The proposed approachfor copy-move forgery detection is performed with the Speeded Up RobustFeatures (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matchingwith Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogramequalization and single-mage super-resolution. Simulation results proved thefeasibility and the robustness of the pre-processing step in homomorphicdetection and SURF detection algorithms for splicing and copy-move forgerydetection, respectively.
