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
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
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
目的随着人脸图像合成技术的快速发展,基于深度学习的人脸伪造技术对社会信息安全的负面影响日益增长。然而,由于不同伪造方法生成的样本之间的数据分布存在较大差异,现有人脸伪造检测方法准确性不高,泛化性较差。为了解决上述问题,提...目的随着人脸图像合成技术的快速发展,基于深度学习的人脸伪造技术对社会信息安全的负面影响日益增长。然而,由于不同伪造方法生成的样本之间的数据分布存在较大差异,现有人脸伪造检测方法准确性不高,泛化性较差。为了解决上述问题,提出一种多元软混合样本驱动的图文对齐人脸伪造检测新方法,充分利用图像与文本的多模态信息对齐,捕捉微弱的人脸伪造痕迹。方法考虑到传统人脸伪造检测方法仅在单一模式的伪造图像上训练,难以应对复杂伪造模式,提出了一种多元软混合的数据增广方式(multivariate and soft blending augmentation,MSBA),促进网络同时捕捉多种伪造模式线索的能力,增强了网络模型对复杂和未知的伪造模式的检测能力。由于不同人脸伪造图像的伪造模式与伪造力度多种多样,导致网络模型真伪检测性能下降。本文基于MSBA方式设计了多元伪造力度估计(multivariate forgery intensity estimation,MFIE)模块,有效针对不同模式和力度的人脸伪造图像进行学习,引导图像编码器提取更加具有泛化性的特征,提高了整体网络框架的检测准确性。结果在域内实验中,与对比算法性能最好的相比,本文方法的准确率(accuracy,ACC)与AUC(area under the curve)指标分别提升3.32%和4.02%。在跨域实验中,本文方法与6种典型方法在5个数据集上进行了性能测试与比较,平均AUC指标提高3.27%。消融实验结果表明本文提出的MSBA方式和MFIE模块对于人脸伪造检测性能的提升均有较好的表现。结论本文面向人脸伪造检测任务设计的CLIP(contrastive language-image pre-training)网络框架大大提高了人脸伪造检测的准确性,提出的MSBA方式和MFIE模块均起到了较好的助力效果,取得了超越已有方法的性能表现。展开更多
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.
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.
文摘目的随着人脸图像合成技术的快速发展,基于深度学习的人脸伪造技术对社会信息安全的负面影响日益增长。然而,由于不同伪造方法生成的样本之间的数据分布存在较大差异,现有人脸伪造检测方法准确性不高,泛化性较差。为了解决上述问题,提出一种多元软混合样本驱动的图文对齐人脸伪造检测新方法,充分利用图像与文本的多模态信息对齐,捕捉微弱的人脸伪造痕迹。方法考虑到传统人脸伪造检测方法仅在单一模式的伪造图像上训练,难以应对复杂伪造模式,提出了一种多元软混合的数据增广方式(multivariate and soft blending augmentation,MSBA),促进网络同时捕捉多种伪造模式线索的能力,增强了网络模型对复杂和未知的伪造模式的检测能力。由于不同人脸伪造图像的伪造模式与伪造力度多种多样,导致网络模型真伪检测性能下降。本文基于MSBA方式设计了多元伪造力度估计(multivariate forgery intensity estimation,MFIE)模块,有效针对不同模式和力度的人脸伪造图像进行学习,引导图像编码器提取更加具有泛化性的特征,提高了整体网络框架的检测准确性。结果在域内实验中,与对比算法性能最好的相比,本文方法的准确率(accuracy,ACC)与AUC(area under the curve)指标分别提升3.32%和4.02%。在跨域实验中,本文方法与6种典型方法在5个数据集上进行了性能测试与比较,平均AUC指标提高3.27%。消融实验结果表明本文提出的MSBA方式和MFIE模块对于人脸伪造检测性能的提升均有较好的表现。结论本文面向人脸伪造检测任务设计的CLIP(contrastive language-image pre-training)网络框架大大提高了人脸伪造检测的准确性,提出的MSBA方式和MFIE模块均起到了较好的助力效果,取得了超越已有方法的性能表现。
