A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions

FrameProv: Towards End-To-End Video Provenance

Video feeds are often deliberately used as evidence, as in the case of CCTV footage; but more often than not, the existence of footage of a supposed event is perceived as proof of fact in the eyes of the public at large. This reliance represents a societal vulnerability given the existence of easy-to-use editing tools and means to fabricate entire video feeds using machine learning. And, as the recent barrage of fake news and fake porn videos have shown, this isn't merely an academic concern, it is actively been exploited. I posit that this exploitation is only going to get more insidious. In this position paper, I introduce a long term project that aims to mitigate some of the most egregious forms of manipulation by embedding trustworthy components in the video transmission chain. Unlike earlier works, I am not aiming to do tamper detection or other forms of forensics -- approaches I think are bound to fail in the face of the reality of necessary editing and compression -- instead, the aim here is to provide a way for the video publisher to prove the integrity of the video feed as well as make explicit any edits they may have performed. To do this, I present a novel data structure, a video-edit specification language and supporting infrastructure that provides end-to-end video provenance, from the camera sensor to the viewer. I have implemented a prototype of this system and am in talks with journalists and video editors to discuss the best ways forward with introducing this idea to the mainstream

Identification and Exploitation of Inadvertent Spectral Artifacts in Digital Audio

We show that modulation products from local oscillators in a variety of commercial camcorders are coupled into the recorded audio track, creating narrow band time invariant spectral features. These spectral features, left largely intact by transcoding, compression and other forms of audiovisual post processing, can encode characteristics of specific camcorders used to capture the audio files, including the make and model. Using data sets both downloaded from YouTube and collected under controlled laboratory conditions we demonstrate an average probability of detection (Pd) approaching 0.95 for identification of a specific camcorder in a population of thousands of similar recordings, with a probability of false alarm (Pfa) of about 0.11. We also demonstrate an average Pd of about 0.93 for correct association of make and model of camcorder based on comparison of audio spectral features extracted from random YouTube downloads compared to a reference library of spectral features captured from known makes and models of camcorders, with a Pfa of 0.06. The method described can be used independently or synergistically with image plane-based techniques such as those based upon Photo Response Non-Uniformity