19,555 research outputs found
Detecting Manipulations in Video
This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future
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
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms
Video Manipulation Techniques for the Protection of Privacy in Remote Presence Systems
Systems that give control of a mobile robot to a remote user raise privacy
concerns about what the remote user can see and do through the robot. We aim to
preserve some of that privacy by manipulating the video data that the remote
user sees. Through two user studies, we explore the effectiveness of different
video manipulation techniques at providing different types of privacy. We
simultaneously examine task performance in the presence of privacy protection.
In the first study, participants were asked to watch a video captured by a
robot exploring an office environment and to complete a series of observational
tasks under differing video manipulation conditions. Our results show that
using manipulations of the video stream can lead to fewer privacy violations
for different privacy types. Through a second user study, it was demonstrated
that these privacy-protecting techniques were effective without diminishing the
task performance of the remote user.Comment: 14 pages, 8 figure
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