16,871 research outputs found
An Evaluation of Popular Copy-Move Forgery Detection Approaches
A copy-move forgery is created by copying and pasting content within the same
image, and potentially post-processing it. In recent years, the detection of
copy-move forgeries has become one of the most actively researched topics in
blind image forensics. A considerable number of different algorithms have been
proposed focusing on different types of postprocessed copies. In this paper, we
aim to answer which copy-move forgery detection algorithms and processing steps
(e.g., matching, filtering, outlier detection, affine transformation
estimation) perform best in various postprocessing scenarios. The focus of our
analysis is to evaluate the performance of previously proposed feature sets. We
achieve this by casting existing algorithms in a common pipeline. In this
paper, we examined the 15 most prominent feature sets. We analyzed the
detection performance on a per-image basis and on a per-pixel basis. We created
a challenging real-world copy-move dataset, and a software framework for
systematic image manipulation. Experiments show, that the keypoint-based
features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and
Zernike features perform very well. These feature sets exhibit the best
robustness against various noise sources and downsampling, while reliably
identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper
appeared in IEEE Transaction on Information Forensics and Securit
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
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
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