Adversarial perturbations disrupt DNN-based face detectors under white-box, gray-box, and black-box settings to sabotage training data for AI face synthesis.
Generative adversarial nets,
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DRLN uses residual-on-residual cascading, dense block concatenation, and Laplacian attention to learn multi-scale features for super-resolution with claimed favorable results on standard benchmarks.
citing papers explorer
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Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations
Adversarial perturbations disrupt DNN-based face detectors under white-box, gray-box, and black-box settings to sabotage training data for AI face synthesis.
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Densely Residual Laplacian Super-Resolution
DRLN uses residual-on-residual cascading, dense block concatenation, and Laplacian attention to learn multi-scale features for super-resolution with claimed favorable results on standard benchmarks.