Image Manipulation Detection
14 papers with code • 0 benchmarks • 0 datasets
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Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned.
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.
The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.
Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels
Forensic analysis of digital photo provenance relies on intrinsic traces left in the photograph at the time of its acquisition.
This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel, where state-of-the-art forensic techniques fail.
Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples
We investigate if the random feature selection approach proposed in  to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features.
PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization
To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations.
The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images.