Camera-based Image Forgery Localization using Convolutional Neural Networks

Camera fingerprints are precious tools for a number of image forensics tasks. A well-known example is the photo response non-uniformity (PRNU) noise pattern, a powerful device fingerprint. Here, to address the image forgery localization problem, we rely on noiseprint, a recently proposed CNN-based camera model fingerprint. The CNN is trained to minimize the distance between same-model patches, and maximize the distance otherwise. As a result, the noiseprint accounts for model-related artifacts just like the PRNU accounts for device-related non-uniformities. However, unlike the PRNU, it is only mildly affected by residuals of high-level scene content. The experiments show that the proposed noiseprint-based forgery localization method improves over the PRNU-based reference

An Improved Statistic for the Pooled Triangle Test against PRNU-Copy Attack

We propose a new statistic to improve the pooled version of the triangle test used to combat the fingerprint-copy counter-forensic attack against PRNU-based camera identification [1]. As opposed to the original version of the test, the new statistic exploits the one-tail nature of the test, weighting differently positive and negative deviations from the expected value of the correlation between the image under analysis and the candidate images, i.e., those image suspected to have been used during the attack. The experimental results confirm the superior performance of the new test, especially when the conditions of the test are challenging ones, that is when the number of images used for the fingerprint-copy attack is large and the size of the image under test is small.Comment: submitted to IEEE Signal Processing Letter

Autoencoder with recurrent neural networks for video forgery detection

Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security, and Forensics, January 201

Improved Tampering Localization in Digital image Forensics: Comparative Study Based on Maximal Entropy Random Walk and Multi-Scale Fusion

Nowadays the increasing ease of editing digital photographs has spawned an urgent need for reliable authentication mechanism capable of precise localization of potential malicious forgeries. In this paper we compare two different Techniques to analyze which technique can be used more efficiently in localization of Tampered Region In Digital Image .First Technique is Maximal Entropy Random Walk in which Strong localization property of this random walk will highlight important regions and to diminish the background- even for noisy response maps. Our evaluation will show that the proposed method can significantly perform both the commonly used threshold-based decision, and the recently proposed optimization approach with a Markovian prior. The second Technique which is based on Multi-Scale Fusion will investigate a multi-scale analysis approach which merge multiple candidate tampering maps, obtained from the analysis with different windows, to obtain a single, more efficient tampering map with better localization resolution. We propose three different techniques for multi- scale fusion, and verify their feasibility .In this slant we consider popular tampering scenario to distinguish between singly and doubly compressed region

Source identification in image forensics

Source identification is one of the most important tasks in digital image forensics. In fact, the ability to reliably associate an image with its acquisition device may be crucial both during investigations and before a court of law. For example, one may be interested in proving that a certain photo was taken by his/her camera, in order to claim intellectual property. On the contrary, it may be law enforcement agencies that are interested to trace back the origin of some images, because they violate the law themselves (e.g. do not respect privacy laws), or maybe they point to subjects involved in unlawful and dangerous activities (like terrorism, pedo-pornography, etc). More in general, proving, beyond reasonable doubts, that a photo was taken by a given camera, may be an important element for decisions in court. The key assumption of forensic source identification is that acquisition devices leave traces in the acquired content, and that instances of these traces are specific to the respective (class of) device(s). This kind of traces is present in the so-called device fingerprint. The name stems from the forensic value of human fingerprints. Motivated by the importance of the source identification in digital image forensics community and the need of reliable techniques using device fingerprint, the work developed in the Ph.D. thesis concerns different source identification level, using both feature-based and PRNU-based approach for model and device identification. In addition, it is also shown that counter-forensics methods can easily attack machine learning techniques for image forgery detection. In model identification, an analysis of hand-crafted local features and deep learning ones has been considered for the basic two-class classification problem. In addition, a comparison with the limited knowledge and the blind scenario are presented. Finally, an application of camera model identification on various iris sensor models is conducted. A blind scenario technique that faces the problem of device source identification using the PRNU-based approach is also proposed. With the use of the correlation between single-image sensor noise, a blind two-step source clustering is proposed. In the first step correlation clustering together with ensemble method is used to obtain an initial partition, which is then refined in the second step by means of a Bayesian approach. Experimental results show that this proposal outperforms the state-of-the-art techniques and still give an acceptable performance when considering images downloaded from Facebook