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Found 5 papers, 2 papers with code
Evaluating Adversarial Robustness in the Spatial Frequency Domain
Convolutional Neural Networks (CNNs) have dominated the majority of computer vision tasks.
In Anticipation of Perfect Deepfake: Identity-anchored Artifact-agnostic Detection under Rebalanced Deepfake Detection Protocol
To bridge this gap, we introduce the Rebalanced Deepfake Detection Protocol (RDDP) to stress-test detectors under balanced scenarios where genuine and forged examples bear similar artifacts.
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning
The key feature of RSQS is that the individual samples in a meta-task are subjected to multiple distribution shifts in each meta-task.
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift
Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set.
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack
To improve efficiency, we introduce the limit-aware random gradient-free estimation and the gradient sliding mechanism to estimate the gradient that adheres to the adversarial limit, i. e., the pixel value limitations of the adversarial example.
