2021/06/29 by Mauro Conti, Simone Milani, Conti, Mauro +5 · 1 voice
Computer Science · #Advanced Steganography and Watermarking Techniques #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Cryptography and Security (cs.CR) #Digital Media Forensic Detection #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimedia (cs.MM) #Video Analysis and Summarization #cs.AI #cs.CR #cs.CV #cs.LG #cs.MM
paper · pdf · doi:10.48550/arxiv.2106.15130
openalex publication_date 2021/06/29 · arxiv published 2021/06/29 · arxiv updated 2021/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.