Video manipulation detection plays a vital role in modern multimedia forensics. In particular, double compression detection provides significant clues leading to the video edition history and hinting at potential malevolent manipulation. While such an analysis is well-understood on images, the research on this subject remains lacking in videos and existing methods are not yet able to reliably detect double-compressed videos. This work presents a novel method for identifying double compression in H.264 codec videos. Our technique exploits the periodicity of frame residuals caused by fixed Group of Pictures in the initial compression, and employs an a contrario framework to minimize and control false detections. The proposed method can reliably detect double compression in videos. It does not require threshold tuning, thus enabling automatic detection. The code is available at https://github.com/li-yanhao/gop_detection.

Satellite images are becoming an increasingly important part of our world. Such images are used to forecast the weather, track green house gas emissions, monitor agricultural crop health, and many other applications. Such advances are possible thanks to the free availability of a large number of satellite images. Satellite imagery now plays a key role in many areas, including external security. In this context, it is necessary to question the reliability of this data. Can the authenticity of a satellite image be guaranteed? How can one protect oneself against an entity wishing to hide illegal military material or, conversely, to incite action against another entity by falsely suggesting that it possess such material? If the forensic analysis of photographs has attracted a great deal of academic interest in recent years, this is not yet the case for satellite imagery. In this paper, we propose a methodology to create a very simple but interesting dataset to test the performance of state-of-the-art forensic methods on pristine and manipulated satellite images. Despite the strong performance of such algorithms, satellite images require special attention due to the nature of the images themselves.

JPEG compression is widely used to store digital images and extensive studies analysed its impact on the image quality; in particular the quantization noise and artefacts created by JPEG. Nevertheless, there is little work on the impact of JPEG compression on the noise already present in the image. In this paper, we propose a model predicting how the noise power is affected by JPEG compression. This allows for a better understanding the noise traces on the image, which is crucial for image forensic analysis and image restoration. An interactive demo for this article is available at https://ipolcore.ipol.im/demo/clientApp/demo.html?id=77777000136

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.

We propose 4Point (Forensics with Positional Internal Training), an unsupervised neural network trained to assess the consistency of the image colour mosaic to find forgeries. Positional learning trains the model to learn the modulo-2 position of pixels, leveraging the translation-invariance of CNN to replicate the underlying mosaic and its potential inconsistencies. Internal learning on a single potentially forged image improves adaption and robustness to varied post-processing and counter-forensics measures. This solution beats existing mosaic detection methods, is more robust to various post-processing and counter-forensic artefacts such as JPEG compression, and can exploit traces to which state-of-the-art generic neural networks are blind. Check qbammey.github.io/4point for the code.

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.

Automatically finding suspicious regions in a potentially forged image by splicing, inpainting or copy-move remains a widely open problem. Blind detection neural networks trained on benchmark data are flourishing. Yet, these methods do not provide an explanation of their detections. The more traditional methods try to provide such evidence by pointing out local inconsistencies in the image noise, JPEG compression, chromatic aberration, or in the mosaic. In this paper we develop a blind method that can train directly on unlabelled and potentially forged images to point out local mosaic inconsistencies. To this aim we designed a CNN structure inspired from demosaicing algorithms and directed at classifying image blocks by their position in the image modulo (2 × 2). Creating a diversified benchmark database using varied demosaicing methods, we explore the efficiency of the method and its ability to adapt quickly to any new data.