Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System

The last-generation video conferencing software allows users to utilize a virtual background to conceal their personal environment due to privacy concerns, especially in official meetings with other employers. On the other hand, users maybe want to fool people in the meeting by considering the virtual background to conceal where they are. In this case, developing tools to understand the virtual background utilize for fooling people in meeting plays an important role. Besides, such detectors must prove robust against different kinds of attacks since a malicious user can fool the detector by applying a set of adversarial editing steps on the video to conceal any revealing footprint. In this paper, we study the feasibility of an efficient tool to detect whether a videoconferencing user background is real. In particular, we provide the first tool which computes pixel co-occurrences matrices and uses them to search for inconsistencies among spectral and spatial bands. Our experiments confirm that cross co-occurrences matrices improve the robustness of the detector against different kinds of attacks. This work's performance is especially noteworthy with regard to color SPAM features. Moreover, the performance especially is significant with regard to robustness versus post-processing, like geometric transformations, filtering, contrast enhancement, and JPEG compression with different quality factors.

VIPPrint: A Large Scale Dataset of Printed and Scanned Images for Synthetic Face Images Detection and Source Linking

The possibility of carrying out a meaningful forensics analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography pictures, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

A Survey of Machine Learning Techniques in Adversarial Image Forensics

Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are also a number of limitations and vulnerabilities associated with machine learning-based approaches, for example how to detect adversarial (image) examples, with real-world consequences (e.g., inadmissible evidence, or wrongful conviction). Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios.

CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis

Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natural images thus contributing to preserve the trustworthiness of digital images. While modern GAN models can generate very high-quality images with no visible spatial artifacts, reconstruction of consistent relationships among colour channels is expectedly more difficult. In this paper, we propose a method for distinguishing GAN-generated from natural images by exploiting inconsistencies among spectral bands, with specific focus on the generation of synthetic face images. Specifically, we use cross-band co-occurrence matrices, in addition to spatial co-occurrence matrices, as input to a CNN model, which is trained to distinguish between real and synthetic faces. The results of our experiments confirm the goodness of our approach which outperforms a similar detection technique based on intra-band spatial co-occurrences only. The performance gain is particularly significant with regard to robustness against post-processing, like geometric transformations, filtering and contrast manipulations.

CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing

Detection of contrast adjustments in the presence of JPEG postprocessing is known to be a challenging task. JPEG post processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack, when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression. The proposed system relies on a patch-based Convolutional Neural Network (CNN), trained to distinguish pristine images from contrast adjusted images, for some selected adjustment operators of different nature. Robustness to JPEG compression is achieved by training the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.