1,304 research outputs found
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
Autoencoder with recurrent neural networks for video forgery detection
Video forgery detection is becoming an important issue in recent years,
because modern editing software provide powerful and easy-to-use tools to
manipulate videos. In this paper we propose to perform detection by means of
deep learning, with an architecture based on autoencoders and recurrent neural
networks. A training phase on a few pristine frames allows the autoencoder to
learn an intrinsic model of the source. Then, forged material is singled out as
anomalous, as it does not fit the learned model, and is encoded with a large
reconstruction error. Recursive networks, implemented with the long short-term
memory model, are used to exploit temporal dependencies. Preliminary results on
forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security,
and Forensics, January 201
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
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