When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm
This highlights new and potentially greater safety challenges for real-world applications of generative AI, such as content moderation and fake image detection, indicating an incremental analysis of risks in an emerging paradigm.
The paper tackles the safety risks of multimodal large language models (MLLMs) in image generation, finding that they generate more unsafe images than diffusion models and produce fake images that are harder for detectors to identify, with detectors still bypassed by longer inputs.
Recently, multimodal large language models (MLLMs) have emerged as a unified paradigm for language and image generation. Compared with diffusion models, MLLMs possess a much stronger capability for semantic understanding, enabling them to process more complex textual inputs and comprehend richer contextual meanings. However, this enhanced semantic ability may also introduce new and potentially greater safety risks. Taking diffusion models as a reference point, we systematically analyze and compare the safety risks of emerging MLLMs along two dimensions: unsafe content generation and fake image synthesis. Across multiple unsafe generation benchmark datasets, we observe that MLLMs tend to generate more unsafe images than diffusion models. This difference partly arises because diffusion models often fail to interpret abstract prompts, producing corrupted outputs, whereas MLLMs can comprehend these prompts and generate unsafe content. For current advanced fake image detectors, MLLM-generated images are also notably harder to identify. Even when detectors are retrained with MLLMs-specific data, they can still be bypassed by simply providing MLLMs with longer and more descriptive inputs. Our measurements indicate that the emerging safety risks of the cutting-edge generative paradigm, MLLMs, have not been sufficiently recognized, posing new challenges to real-world safety.