MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection
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by
Chengbo Dong, Xinru Chen, Ruohan Hu, Juan Cao, Xirong Li
2022
Abstract
As manipulating images by copy-move, splicing and/or inpainting may lead to
misinterpretation of the visual content, detecting these sorts of manipulations
is crucial for media forensics. Given the variety of possible attacks on the
content, devising a generic method is nontrivial. Current deep learning based
methods are promising when training and test data are well aligned, but perform
poorly on independent tests. Moreover, due to the absence of authentic test
images, their image-level detection specificity is in doubt. The key question
is how to design and train a deep neural network capable of learning
generalizable features sensitive to manipulations in novel data, whilst
specific to prevent false alarms on the authentic. We propose multi-view
feature learning to jointly exploit tampering boundary artifacts and the noise
view of the input image. As both clues are meant to be semantic-agnostic, the
learned features are thus generalizable. For effectively learning from
authentic images, we train with multi-scale (pixel / edge / image) supervision.
We term the new network MVSS-Net and its enhanced version MVSS-Net++.
Experiments are conducted in both within-dataset and cross-dataset scenarios,
showing that MVSS-Net++ performs the best, and exhibits better robustness
against JPEG compression, Gaussian blur and screenshot based image
re-capturing.
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