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

Beyond the pixels: learning and utilising video compression features for localisation of digital tampering.

Video compression is pervasive in digital society. With rising usage of deep convolutional neural networks (CNNs) in the fields of computer vision, video analysis and video tampering detection, it is important to investigate how patterns invisible to human eyes may be influencing modern computer vision techniques and how they can be used advantageously. This work thoroughly explores how video compression influences accuracy of CNNs and shows how optimal performance is achieved when compression levels in the training set closely match those of the test set. A novel method is then developed, using CNNs, to derive compression features directly from the pixels of video frames. It is then shown that these features can be readily used to detect inauthentic video content with good accuracy across multiple different video tampering techniques. Moreover, the ability to explain these features allows predictions to be made about their effectiveness against future tampering methods. The problem is motivated with a novel investigation into recent video manipulation methods, which shows that there is a consistent drive to produce convincing, photorealistic, manipulated or synthetic video. Humans, blind to the presence of video tampering, are also blind to the type of tampering. New detection techniques are required and, in order to compensate for human limitations, they should be broadly applicable to multiple tampering types. This thesis details the steps necessary to develop and evaluate such techniques

Digital Multimedia Forensics and Anti-Forensics

As the use of digital multimedia content such as images and video has increased, so has the means and the incentive to create digital forgeries. Presently, powerful editing software allows forgers to create perceptually convincing digital forgeries. Accordingly, there is a great need for techniques capable of authenticating digital multimedia content. In response to this, researchers have begun developing digital forensic techniques capable of identifying digital forgeries. These forensic techniques operate by detecting imperceptible traces left by editing operations in digital multimedia content. In this dissertation, we propose several new digital forensic techniques to detect evidence of editing in digital multimedia content. We begin by identifying the fingerprints left by pixel value mappings and show how these can be used to detect the use of contrast enhancement in images. We use these fingerprints to perform a number of additional forensic tasks such as identifying cut-and-paste forgeries, detecting the addition of noise to previously JPEG compressed images, and estimating the contrast enhancement mapping used to alter an image. Additionally, we consider the problem of multimedia security from the forger's point of view. We demonstrate that an intelligent forger can design anti-forensic operations to hide editing fingerprints and fool forensic techniques. We propose an anti-forensic technique to remove compression fingerprints from digital images and show that this technique can be used to fool several state-of-the-art forensic algorithms. We examine the problem of detecting frame deletion in digital video and develop both a technique to detect frame deletion and an anti-forensic technique to hide frame deletion fingerprints. We show that this anti-forensic operation leaves behind fingerprints of its own and propose a technique to detect the use of frame deletion anti-forensics. The ability of a forensic investigator to detect both editing and the use of anti-forensics results in a dynamic interplay between the forger and forensic investigator. We use develop a game theoretic framework to analyze this interplay and identify the set of actions that each party will rationally choose. Additionally, we show that anti-forensics can be used protect against reverse engineering. To demonstrate this, we propose an anti-forensic module that can be integrated into digital cameras to protect color interpolation methods

Fundamental Limits in Multimedia Forensics and Anti-forensics

As the use of multimedia editing tools increases, people become questioning the authenticity of multimedia content. This is specially a big concern for authorities, such as law enforcement, news reporter and government, who constantly use multimedia evidence to make critical decisions. To verify the authenticity of multimedia content, many forensic techniques have been proposed to identify the processing history of multimedia content under question. However, as new technologies emerge and more complicated scenarios are considered, the limitation of multimedia forensics has been gradually realized by forensic researchers. It is the inevitable trend in multimedia forensics to explore the fundamental limits. In this dissertation, we propose several theoretical frameworks to study the fundamental limits in various forensic problems. Specifically, we begin by developing empirical forensic techniques to deal with the limitation of existing techniques due to the emergence of new technology, compressive sensing. Then, we go one step further to explore the fundamental limit of forensic performance. Two types of forensic problems have been examined. In operation forensics, we propose an information theoretical framework and define forensicability as the maximum information features contain about hypotheses of processing histories. Based on this framework, we have found the maximum number of JPEG compressions one can detect. In order forensics, an information theoretical criterion is proposed to determine when we can and cannot detect the order of manipulation operations that have been applied on multimedia content. Additionally, we have examined the fundamental tradeoffs in multimedia antiforensics, where attacking techniques are developed by forgers to conceal manipulation fingerprints and confuse forensic investigations. In this field, we have defined concealability as the effectiveness of anti-forensics concealing manipulation fingerprints. Then, a tradeoff between concealability, rate and distortion is proposed and characterized for compression anti-forensics, which provides us valuable insights of how forgers may behave under their best strategy

Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights