Removal and injection of keypoints for SIFT-based copy-move counter-forensics

Recent studies exposed the weaknesses of scale-invariant feature transform (SIFT)-based analysis by removing keypoints without significantly deteriorating the visual quality of the counterfeited image. As a consequence, an attacker can leverage on such weaknesses to impair or directly bypass with alarming efficacy some applications that rely on SIFT. In this paper, we further investigate this topic by addressing the dual problem of keypoint removal, i.e., the injection of fake SIFT keypoints in an image whose authentic keypoints have been previously deleted. Our interest stemmed from the consideration that an image with too few keypoints is per se a clue of counterfeit, which can be used by the forensic analyst to reveal the removal attack. Therefore, we analyse five injection tools reducing the perceptibility of keypoint removal and compare them experimentally. The results are encouraging and show that injection is feasible without causing a successive detection at SIFT matching level. To demonstrate the practical effectiveness of our procedure, we apply the best performing tool to create a forensically undetectable copy-move forgery, whereby traces of keypoint removal are hidden by means of keypoint injection

Counter-forensics of SIFT-based copy-move detection by means of keypoint classification

Copy-move forgeries are very common image manipulations that are often carried out with malicious intents. Among the techniques devised by the 'Image Forensic' community, those relying on scale invariant feature transform (SIFT) features are the most effective ones. In this paper, we approach the copy-move scenario from the perspective of an attacker whose goal is to remove such features. The attacks conceived so far against SIFT-based forensic techniques implicitly assume that all SIFT keypoints have similar properties. On the contrary, we base our attacking strategy on the observation that it is possible to classify them in different typologies. Also, one may devise attacks tailored to each specific SIFT class, thus improving the performance in terms of removal rate and visual quality. To validate our ideas, we propose to use a SIFT classification scheme based on the gray scale histogram of the neighborhood of SIFT keypoints. Once the classification is performed, we then attack the different classes by means of class-specific methods. Our experiments lead to three interesting results: (1) there is a significant advantage in using SIFT classification, (2) the classification-based attack is robust against different SIFT implementations, and (3) we are able to impair a state-of-the-art SIFT-based copy-move detector in realistic cases

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

SIFT keypoint removal and injection for countering matching-based image forensics

Scale Invariant Feature Transform (SIFT) has been widely employed in several image application domains, including Image Forensics (e.g. detection of copy-move forgery or near duplicates). Until now, the research community has focused on studying the robustness of SIFT against legitimate image processing, but rarely concerned itself with the problem of SIFT security against malicious procedures. Recently, a number of methods allowing to remove SIFT keypoints from an original image have been devised. Although quite effective, such methods produce an attacked image with very few (or no) keypoints, thus leaving cues that can be easily exploited by a forensic analyst to reveal the occurred manipulation. In this paper, we explore the topic of reintroducing fake SIFT keypoints into a previously cleaned image in order to address the main weakness of the existing removal attacks. In particular, we evaluate the fitness of locally adaptive contrast enhancement methods to the task of injecting new keypoints. The results we obtained are encouraging: (i) it is possible to effectively introduce new keypoints whose descriptors do not match with those of the original image, thus concealing the removal forgery; (ii) the perceptual quality of the image following the removal and injection attacks is comparable to the one of the original image