Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
A natural image model approach to splicing detection
Proceedings of the 9th workshop on Multimedia & security
Steganalysis Versus Splicing Detection
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Anti-forensics of contrast enhancement in digital images
Proceedings of the 12th ACM workshop on Multimedia and security
Detection of copy-rotate-move forgery using Zernike moments
IH'10 Proceedings of the 12th international conference on Information hiding
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Hiding Traces of Resampling in Digital Images
IEEE Transactions on Information Forensics and Security
Detection of Double-Compression in JPEG Images for Applications in Steganography
IEEE Transactions on Information Forensics and Security
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In this paper, we point out state-of-the-art algorithm in natural image splicing detection, namely the transition probability matrix feature proposed by Shi, et al., can be attacked by modifying block discrete cosine transform (BDCT) coefficients without significantly degrading quality of the spliced image. BDCT coefficients of the spliced image are modified so that its distance to a close authentic image in feature space is minimized. The minimization is accomplished with a greedy algorithm. The modification makes the spliced image statistically similar to the authentic image so as to reduce the effectiveness of detection algorithm. The performance of the algorithm is evaluated on Columbia Image Splicing Detection Evaluation Dataset. With the proposed anti-forensics post processing, detection accuracy and true positive rate reduces to 69.4% and 62.5% respectively, while the processed images still maintain average peak signal-to-noise ratio (PSNR) at 42.22db.