Optimizing Input of Denoising Score Matching is Biased Towards Higher Score Norm

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.

The article discusses advancements in multi-target adversarial attacks, highlighting the limitations of current generative methods that use one-dimensional tensors for target label encoding. It emphasizes the importance of both the quality and quantity of semantic features in enhancing the transferability of these attacks. A new framework, 2D Tensor-Guided Adversarial Fusion (TGAF), is proposed to improve the encoding process by leveraging diffusion models, ensuring that generated noise retains complete semantic information.