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Found 7 papers, 2 papers with code
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
We verify the effectiveness of the proposed explanations from two aspects: (1) Counterfactual Trace Visualization: the enhanced forgery images are useful to reveal artifacts by visually contrasting the original images and two different visualization methods; (2) Transferable Adversarial Attacks: the adversarial forgery images generated by attacking the detection model are able to mislead other detection models, implying the removed artifacts are general.
Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?
To further investigate natural triggers, we propose a novel analysis-by-synthesis backdoor attack against face forgery detection models, which embeds natural triggers in the latent space.
3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification.
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models
These fine-grained adversarial examples can be used for selecting robust backbone networks and auxiliary features.
Transferable Sparse Adversarial Attack
The experiment shows that our method has improved the transferability by a large margin under a similar sparsity setting compared with state-of-the-art methods.
Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition
In this paper, we demonstrate that the state-of-the-art gait recognition model is vulnerable to such attacks.
A New Ensemble Method for Concessively Targeted Multi-model Attack
Thus, in this paper, we propose a new attack mechanism which performs the non-targeted attack when the targeted attack fails.
