CNN-based fast source device identification

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this paper we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard approaches

Security and Forensics Exploration of Learning-based Image Coding

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG-AI) and MJoint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy

Security and forensics exploration of learning-based image coding

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG AI) and Joint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy

User profiles’ image clustering for digital investigations

Sharing images on Social Network (SN) platforms is one of the most widespread behaviors which may cause privacy-intrusive and illegal content to be widely distributed. Clustering the images shared through SN platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The Sensor Pattern Noise (SPN) caused by camera sensor imperfections due to the manufacturing process has been proved to be an effective and robust camera fingerprint that can be used for several tasks, such as digital evidence analysis, smartphone fingerprinting and user profile linking as well. Clustering the images uploaded by users on their profiles is a way of fingerprinting the camera sources and it is considered a challenging task since users may upload different types of images, i.e., the images taken by users’ smartphones (taken images) and single images from different sources, cropped images, or generic images from the Web (shared images). The shared images make a perturbation in the clustering task, as they do not usually present sufficient characteristics of SPN of their related sources. Moreover, they are not directly referable to the user’s device so they have to be detected and removed from the clustering process. In this paper, we propose a user profiles’ image clustering method without prior knowledge about the type and number of the camera sources. The hierarchical graph-based method clusters both types of images, taken images and shared images. The strengths of our method include overcoming large-scale image datasets, the presence of shared images that perturb the clustering process and the loss of image details caused by the process of content compression on SN platforms. The method is evaluated on the VISION dataset, which is a public benchmark including images from 35 smartphones. The dataset is perturbed by 3000 images, simulating the shared images from different sources except for users’ smartphones. Experimental results confirm the robustness of the proposed method against perturbed datasets and its effectiveness in the image clustering

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features

Deepfake Detection: A Comprehensive Study from the Reliability Perspective

The mushroomed Deepfake synthetic materials circulated on the internet have raised serious social impact to politicians, celebrities, and every human being on earth. In this paper, we provide a thorough review of the existing models following the development history of the Deepfake detection studies and define the research challenges of Deepfake detection in three aspects, namely, transferability, interpretability, and reliability. While the transferability and interpretability challenges have both been frequently discussed and attempted to solve with quantitative evaluations, the reliability issue has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake related cases in court. We therefore conduct a model reliability study scheme using statistical random sampling knowledge and the publicly available benchmark datasets to qualitatively validate the detection performance of the existing models on arbitrary Deepfake candidate suspects. A barely remarked systematic data pre-processing procedure is demonstrated along with the fair training and testing experiments on the existing detection models. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of reliably qualified detection models. The model reliability study provides a workflow for the detection models to act as or assist evidence for Deepfake forensic investigation in court once approved by authentication experts or institutions.Comment: 20 pages for peer revie

Blind PRNU-Based Image Clustering for Source Identification

We address the problem of clustering a set of images, according to their source device, in the absence of any prior information. Image similarity is computed based on noise residuals, regarded as single-image estimates of the camera’s photo-response non-uniformity (PRNU) pattern. First, residuals are grouped by correlation clustering, and several alternative data partitions are computed as a function of a running decision boundary. Then, these partitions are processed jointly to extract a single, more reliable, consensus clustering and, with it, more reliable PRNU estimates. Finally, both clustering and PRNU estimates are progressively refined by merging pairs of the same- PRNU clusters, selected on the basis of a maximum-likelihood ratio statistic. Extensive experiments prove the proposed method to outperform the current state of the art both on pristine images and compressed images downloaded from social networks. A remarkable feature of the method is that it does not require the user to set any parameter, nor to provide a training set to estimate them. Moreover, through a suitable choice of basic tools, and efficient implementation, complexity remains always quite limited