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Found 5 papers, 4 papers with code
STEREO: Towards Adversarially Robust Concept Erasing from Text-to-Image Generation Models
Though many methods have been proposed for erasing undesired concepts from T2IG models, they only provide a false sense of security, as recent works demonstrate that concept-erased models (CEMs) can be easily deceived to generate the erased concept through adversarial attacks.
FLIP: Cross-domain Face Anti-spoofing with Language Guidance
Specifically, we show that aligning the image representation with an ensemble of class descriptions (based on natural language semantics) improves FAS generalizability in low-data regimes.
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection
In FedSIS, a hybrid Vision Transformer (ViT) architecture is learned using a combination of FL and split learning to achieve robustness against statistical heterogeneity in the client data distributions without any sharing of raw data (thereby preserving privacy).
Evading Forensic Classifiers with Attribute-Conditioned Adversarial Faces
While these forensic models can detect whether a face image is synthetic or real with high accuracy, they are also vulnerable to adversarial attacks.
DEXTER: An end-to-end system to extract table contents from electronic medical health documents
We design a conventional computer vision-based approach for table type classification and cell detection using parameterized kernels based on image size for detecting rows and columns.
