5 accepted papers
Concept erasure helps stop diffusion models (DMs) from generating harmful content; but current methods face robustness-retention trade-off. **Robustness** means the model fine-tuned by concept erasure methods resists reactivation of erased concepts, even under semantically related prompts. **Retenti…
Large language models trained on vast corpora inherently risk memorizing sensitive or harmful content, which may later resurface in their outputs. Prevailing unlearning methods generally rely on gradient ascent and its variants to lower the probability of specific target responses. However, we find…
Advances in generative AI (GenAI) have increasingly complicated the identification of synthetic images, prompting the proposal of numerous zero-/few-shot detection methods to counter unknown GenAI better. However, we observe that existing detectors often misclassify synthetic images with physical tr
To protect deep neural networks (DNNs) from adversarial attacks, adversarial training (AT) is developed by incorporating adversarial examples (AEs) into model training. Recent studies show that adversarial attacks disproportionately impact the patterns within the phase of the sample's frequency spec…
The deep model training procedure requires large-scale datasets of annotated data. Due to the difficulty of annotating a large number of samples, label noise caused by incorrect annotations is inevitable, resulting in low model performance and poor model generalization. To combat label noise, curren…