Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017

Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts

In this paper, we propose a forensic algorithm to discriminate between original and forged regions in JPEG images, under the hypothesis that the tampered image presents a double JPEG compression, either aligned (A-DJPG) or non-aligned (NA-DJPG). Unlike previous approaches, the proposed algorithm does not need to manually select a suspect region in order to test the presence or the absence of double compression artifacts. Based on an improved and unified statistical model characterizing the artifacts that appear in the presence of both A-DJPG or NA-DJPG, the proposed algorithm automatically computes a likelihood map indicating the probability for each $8 \times 8$ discrete cosine transform block of being doubly compressed. The validity of the proposed approach has been assessed by evaluating the performance of a detector based on thresholding the likelihood map, considering different forensic scenarios. The effectiveness of the proposed method is also confirmed by tests carried on realistic tampered images. An interesting property of the proposed Bayesian approach is that it can be easily extended to work with traces left by other kinds of processin

Image statistical frameworks for digital image forensics

The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology. These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows. First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images. Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor. Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)

Digital Forensic Technique for Multiple Compression based JPEG Forgery

In today's digital world digital multimedia like, images, voice-notes and videos etc., are the major source of information/data exchange. The authenticity of these multimedia is greatly vital in the legitimate business, media world and broadcast industry. However, with enormous multiplication of ease, simple-to-utilize data manipulation tools and softwares lead to the faithfulness of digital images is in question. In our work, we propose a technique to identify digital forgery or tampering in JPEG (Joint Photographic Experts Group) images which are based on multiple compression factor. We are dealing with the JPEG images on the grounds because JPEG is the standard storage format used in almost all present day digital devices like digital camera, camcorder, mobile devices and other image acquisition devices. JPEG compresses a image to the best compression in-order to manage the storage requirement. JPEG is a lossy compression standard. At the point when an assailant or criminal modifies some region/part of a JPEG image by any image processing tools and save it, the modified region of the image is doubly-compressed. In our work, we exploit this multiple compression in JPEG images to distinguish digital forgery or falsification

Localization of JPEG double compression through multi-domain convolutional neural networks

When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.Comment: Accepted to CVPRW 2017, Workshop on Media Forensic

An Overview on Image Forensics

The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes