A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelets: a tutorial in theory and applications
Wavelets: a tutorial in theory and applications
Communications of the ACM
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
A survey of watermarking algorithms for image authentication
EURASIP Journal on Applied Signal Processing
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Blind Authentication Using Periodic Properties of Interpolation
IEEE Transactions on Information Forensics and Security
A bibliography on blind methods for identifying image forgery
Image Communication
Proceedings of the 2010 ACM workshop on Surreal media and virtual cloning
Passive detection of paint-doctored JPEG images
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Detecting digital image splicing in chroma spaces
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Exposing image forgery with blind noise estimation
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
Robust copy-move image forgery detection using undecimated wavelets and Zernike moments
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
A non-intrusive method for copy-move forgery detection
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
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A commonly used tool to conceal the traces of tampering is the addition of locally random noise to the altered image regions. The noise degradation is the main cause of failure of many active or passive image forgery detection methods. Typically, the amount of noise is uniform across the entire authentic image. Adding locally random noise may cause inconsistencies in the image's noise. Therefore, the detection of various noise levels in an image may signify tampering. In this paper, we propose a novel method capable of dividing an investigated image into various partitions with homogenous noise levels. In other words, we introduce a segmentation method detecting changes in noise level. We assume the additive white Gaussian noise. Several examples are shown to demonstrate the proposed method's output. An extensive quantitative measure of the efficiency of the noise estimation part as a function of different noise standard deviations, region sizes and various JPEG compression qualities is proposed as well.