This work describes a method for detecting JPEG compression as well as its grid origin. The JPEG algorithm performs a quantization of the DCT coefficients of non-overlapping 8×8 blocks of images, setting many of those coefficients to zero. The method described here exploits these facts and identifies the presence of a JPEG grid when a significant number of DCT zeros is observed for a given grid origin. This method can be applied globally to identify a JPEG compression, and also locally to identify image forgeries when misaligned or missing JPEG grids are found. The algorithm includes a statistical validation step according to Desolneux, Moisan and Morel's a contrario theory, which associates a number of false alarms (NFA) with each tampering detection. Detections are obtained by a threshold of the NFA, which renders the method fully automatic and endows it with a false alarm control mechanism. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instruction are included in the README.txt file of the archive.

We propose a new method to evaluate image forensics tools, that characterizes what image cues are being used by each detector. Our method enables effortless creation of an arbitrarily large dataset of carefully tampered images in which controlled detection cues are present. Starting with raw images, we alter aspects of the image formation pipeline inside a mask, while leaving the rest of the image intact. This does not change the image's interpretation; we thus call such alterations "non-semantic", as they yield no semantic inconsistencies. This method avoids the painful and often biased creation of convincing semantics. All aspects of image formation (noise, CFA, compression pattern and quality, etc.) can vary independently in both the authentic and tampered parts of the image. Alteration of a specific cue enables precise evaluation of the many forgery detectors that rely on this cue, and of the sensitivity of more generic forensic tools to each specific trace of forgery, and can be used to guide the combination of different methods. Based on this methodology, we create a database and conduct an evaluation of the main state-of-the-art image forensics tools, where we characterize the performance of each method with respect to each detection cue. Check qbammey.github.io/trace for the database and code.GALILEO: How would it be if your Highness were now to observe these impossible as well as unnecessary stars through this telescope? THE MATHEMATICIAN: One might be tempted to reply that your telescope, showing something which cannot exist, may not be a very reliable telescope, eh?

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Image JPEG compression leaves blocking artifact traces. This paper describes an algorithm that exploits those traces to locally recover the grid embedded in the image by the JPEG compression. The algorithm returns a list of grids associated with different parts of the image. The method uses Chen and Hsu's cross-difference to reveal the artifacts. Then, an a contrario validation step according to Desolneux, Moisan and Morel's theory delivers for each detected grid a Number of False Alarms (NFA) which tells how unlikely it is that the detection is due to chance. The only parameter is the step size of the windows used, which represents the exhaustiveness of the method. The application to image forgery detection is twofold: first, the presence of discrepant JPEG grids with low NFA is a strong forgery cue; second, knowledge of the grid is anyway required for further JPEG forensic analysis. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instruction are included in the README.txt file of the archive.

We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then Ponomarenko's method is extended by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on three datasets show that this method improves on the state of the art.