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Robust Retraining-free GAN Fingerprinting via Personalized Normalization
In recent years, there has been significant growth in the commercial applications of generative models, licensed and distributed by model developers to users, who in turn use them to offer services.
Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs
We present the results of extensive experiments showing that the presence of the watermark has a negligible impact on the quality of the generated images, and proving the superior robustness of the watermark against model modification and surrogate model attacks.
Learning to mask: Towards generalized face forgery detection
Our goal is to reduce the features that are easy to learn in the training phase, so as to reduce the risk of overfitting on specific forgery types.
General GAN-generated image detection by data augmentation in fingerprint domain
To our knowledge, we are the first to conduct data augmentation in the fingerprint domain.
Learning Second Order Local Anomaly for General Face Forgery Detection
We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies.
Supervised GAN Watermarking for Intellectual Property Protection
The aim is to watermark the GAN model so that any image generated by the GAN contains an invisible watermark (signature), whose presence inside the image can be checked at a later stage for ownership verification.
A Robust Document Image Watermarking Scheme using Deep Neural Network
Then the identity can be extracted to prove the copyright from the watermarked carrier even after suffering various attacks.
