Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Detecting Digital Forgeries Using Bispectral Analysis
Detecting Digital Forgeries Using Bispectral Analysis
Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue
Proceedings of the 10th ACM workshop on Multimedia and security
An efficient and robust method for detecting copy-move forgery
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Proceedings of the 11th ACM workshop on Multimedia and security
A new approach for JPEG resize and image splicing detection
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Novel stream mining for audio steganalysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Improved detection and evaluation for JPEG steganalysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Exposing digital forgeries from JPEG ghosts
IEEE Transactions on Information Forensics and Security
Temporal derivative-based spectrum and mel-cepstrum audio steganalysis
IEEE Transactions on Information Forensics and Security
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
Image tamper detection based on demosaicing artifacts
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Neighboring joint density-based JPEG steganalysis
ACM Transactions on Intelligent Systems and Technology (TIST)
Detecting double JPEG compression with the same quantization matrix
IEEE Transactions on Information Forensics and Security
Region duplication detection using image feature matching
IEEE Transactions on Information Forensics and Security
Derivative-based audio steganalysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A method to detect JPEG-based double compression
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Steganalysis of DCT-embedding based adaptive steganography and YASS
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
IH'04 Proceedings of the 6th international conference on Information Hiding
Statistical tools for digital forensics
IH'04 Proceedings of the 6th international conference on Information Hiding
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Detection of Double-Compression in JPEG Images for Applications in Steganography
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security
Steganalysis of DCT-embedding based adaptive steganography and YASS
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
Third ACM international workshop on multimedia in forensics and intelligence (MiFor 2011)
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Shift recompression-based feature mining for detecting content-aware scaled forgery in JPEG images
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Identification of smartphone-image source and manipulation
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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Image tampering, being widely facilitated and proliferated by today's digital techniques, is increasingly causing problems concerning the authenticity of digital images. As one of the most favorable compressed media, JPEG image can be easily tampered without leaving any visible clues. JPEG-based forensics, including the detection of double compression, interpolation, rotation, etc, has been actively performed. However, the detection of misaligned cropping and recompression, with the same quantization matrix that was once used to encode original JPEG images, has not been effectively expressed or ignored to some extent. Aiming to detect such manipulations for forensics purpose, in this paper, we propose an approach based on block artifacts caused by the manipulation with JPEG compression. Specifically, we propose a shift-recompression based detection method to identify the inconsistency of the block artifacts in doctored JPEG images. The learning classifiers are applied for classification. Experimental results show that our approach is very promising to detect misaligned cropping and recompression with the same quantization matrix and greatly improves the existing methods. Our detection method is also very effective to detect relevant copy-paste and composite forgery in JPEG images.