1,721,114 research outputs found
A DFT-Based Analysis to Discern Between Camera and Scanned Images
No full textDigital images are generated by different sensors, understanding which kind of sensor has acquired a certain image could be crucial in many application scenarios where digital forensic techniques operate. In this
article a new methodology which permits to establish if a digital photo has been taken by a photo-camera
or has been scanned by a scanner is presented. The specific geometrical features of the sensor pattern noise
introduced by the sensor are investigated by resorting to a DFT (Discrete Fourier Transform) analysis and
consequently the origin of the digital content is assessed. Experimental results are provided to witness the
reliability of the proposed technique
Image Watermarking Backdoor Attacks in CNN-Based Classification Tasks
No full textIn these last years, neural networks are becoming the basis for different kinds of applications and this is mainly due to the stunning performances they offer. Nevertheless, all that glitters is not gold: such tools have demonstrated to be highly sensitive to malicious approaches such as gradient manipulation or the injection of adversarial samples. In particular, another kind of attack that can be performed is to poison a neural network during the training time by injecting a perceptually barely visible trigger signal in a small portion of the dataset (target class), to actually create a backdoor into the trained model. Such a backdoor can be then exploited to redirect all the predictions to the chosen target class at test time. In this work, a novel backdoor attack which resorts to image watermarking algorithms to generate a trigger signal is presented. The watermark signal is almost unperceivable and is embedded in a portion of images of the target class; two different watermarking algorithms have been tested. Experimental results carried out on datasets like MNIST and GTSRB provide satisfactory performances in terms of attack success rate and introduced distortion
Image Origin Classification Based on Social Network Provenance
Full text linkRecognizing information about the origin of a digital image has been individuated as a crucial task to be tackled by the image forensic scientific community. Understanding something on the previous history of an image could be strategic to address any successive assessment to be made on it: knowing the kind of device used for acquisition or, better, the model of the camera could focus investigations in a specific direction. Sometimes just revealing that a determined post-processing, such as an interpolation or a filtering, has been performed on an image could be of fundamental importance to go back to its provenance. This paper locates in such a context and proposes an innovative method to inquire if an image derives from a social network and, in particular, try to distinguish from, which one has been downloaded. The technique is based on the assumption that each social network applies a peculiar and mostly unknown manipulation that, however, leaves some distinctive traces on the image; such traces can be extracted to feature every platform. By resorting at trained classifiers, the presented methodology is satisfactorily able to discern different social network origins. Experimental results carried out on diverse image datasets and in various operative conditions witness that such a distinction is possible. In addition, the proposed method is also able to go back to the original JPEG quality factor the image had before being uploaded on a social network. © 2005-2012 IEEE
A DVB-MHP web browser to pursue convergence between Digital Terrestrial Television and Internet
No full textIn the last decade with the growth of Interactive Digital Television (IDTV) we have seen the end of passive television. An example of this trend is Internet access through television by means of the last generation Set Top Boxes (STBs). The chance to enjoy web contents through digital television Set Top Boxes, delivering a satisfying browsing experience across this platform, could provide the opportunity to promote social inclusion and bridging the "digital divide". In this paper we present WebClimb, a web browser that would pursue an effective integration of Digital Terrestrial Television (DTT) and Internet in the DVB-MHP platform. WebClimb is a Java-based web browser that enables users to browse the web by interacting with an asy to use Graphical User Interface (GUI), driven by a common TV remote control without asking for reformatting such a content on the server side. In addition to this, the main requirement has been to design and develop an MHP browser application to be broadcast through a TV channel and not embedded in a specific device, though it could be too. Experimental results and a comparison with other possible solutions are provided
Splicing Forgeries Localization through the Use of First Digit Features
No full textOne of the principal problems in image forensics is determining if a particular image is authentic or not and, if manipulated, to localize which parts have been altered. In fact, localization is basic within the process of image examination because it permits to link the modified zone with the corresponding image area and, above all, with the meaning of it. Forensic instruments dealing with copy-move manipulation quite always provides a localization map, but, on the contrary, only a few tools, devised to detect a splicing operation, are able to give information about localization too. In this paper, a method to distinguish and then localize a single and a double JPEG compression in portions of an image through the use of the DCT coefficients first digit features and employing a Support Vector Machine (SVM) classifier is proposed. Experimental results and a comparison with a state-of-the-art technique are provided to witness the performances offered by the proposed method in terms of forgery localizatio
Counter-forensics of SIFT-based copy-move detection by means of keypoint classification
Full text linkCopy-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
Acquisition source identification through a blind image classification
No full textImage forensics, besides understanding if a digital image has been forged, often aims at determining information about image origin. In particular, it could be worthy to individuate which is the kind of source (digital camera, scanner or computer graphics software) that has generated a certain photo. Such an issue has already been studied in literature, but the problem of doing that in a blind manner has not been faced so far. It is easy to understand that in many application scenarios information at disposal is usually very limited; this is the case when, given a set of L images, the authors want to establish if they belong to K different classes of acquisition sources, without having any previous knowledge about the number of specific types of generation processes. The proposed system is able, in an unsupervised and fast manner, to blindly classify a group of photos without neither any initial information about their membership nor by resorting at a trained classifier. Experimental results have been carried out to verify actual performances of the proposed methodology and a comparative analysis with two SVM-based clustering techniques has been performed too
Tracing images back to their social network of origin: A CNN-based approach
Full text linkRecovering information about the history of a digital content, such as an image or a video, can be strategic to address an investigation from the early stages. Storage devices, smart-phones and PCs, belonging to a suspect, are usually confiscated as soon as a warrant is issued. Any multimedia content found is analyzed in depth, in order to trace back its provenance and, if possible, its original source. This is particularly important when dealing with social networks, where most of the user-generated photos and videos are uploaded and shared daily. Being able to discern if images are downloaded from a social network or directly captured by a digital camera, can be crucial in leading consecutive investigations. In this paper, we propose a novel method based on convolutional neural networks (CNN) to determine the image provenance, whether it originates from a social network, a messaging application or directly from a photo-camera. By considering only the visual content, the method works irrespective of an eventual manipulation of metadata performed by an attacker. We have tested the proposed technique on three publicly available datasets of images downloaded from seven popular social networks, obtaining state-of-the-art results
Localization of JPEG Double Compression Through Multi-domain Convolutional Neural Networks
Full text linkWhen 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
Removal and injection of keypoints for SIFT-based copy-move counter-forensics
Full text linkRecent 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
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