Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach

The paper titled 'Optimizing Input of Denoising Score Matching is Biased Towards Higher Score Norm' discusses the implications of using denoising score matching in optimizing diffusion models. It reveals that this optimization disrupts the equivalence between denoising score matching and exact score matching, resulting in a bias that favors higher score norms. The study also highlights similar biases in optimizing data distributions with pre-trained diffusion models, affecting various applications such as MAR, PerCo, and DreamFusion.

Artificial intelligence (AI) in media has seen rapid advancements over the past decade, particularly with the introduction of Generative Adversarial Networks (GANs) and diffusion models, which have enhanced photorealistic image generation. However, these developments have also led to challenges in distinguishing between real and synthetic content, as evidenced by the rise of deepfakes. Many detection models utilizing deep learning methods like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been created, but they often struggle with generalization and multimodal data.