Linda Laurier

Linda Laurier, Ave Giulietta, Arlo Octavia et al.

The emergence of diffusion models has transformed synthetic media generation, offering unmatched realism and control over content creation. These advancements have driven innovation across fields such as art, design, and scientific visualization. However, they also introduce significant ethical and societal challenges, particularly through the creation of hyper-realistic images that can facilitate deepfakes, misinformation, and unauthorized reproduction of copyrighted material. In response, the need for effective detection mechanisms has become increasingly urgent. This review examines the evolving adversarial relationship between diffusion model development and the advancement of detection methods. We present a thorough analysis of contemporary detection strategies, including frequency and spatial domain techniques, deep learning-based approaches, and hybrid models that combine multiple methodologies. We also highlight the importance of diverse datasets and standardized evaluation metrics in improving detection accuracy and generalizability. Our discussion explores the practical applications of these detection systems in copyright protection, misinformation prevention, and forensic analysis, while also addressing the ethical implications of synthetic media. Finally, we identify key research gaps and propose future directions to enhance the robustness and adaptability of detection methods in line with the rapid advancements of diffusion models. This review emphasizes the necessity of a comprehensive approach to mitigating the risks associated with AI-generated content in an increasingly digital world.

Pete Janowczyk, Linda Laurier, Ave Giulietta et al.

Multi-Modal Language Models (MLLMs) have transformed artificial intelligence by combining visual and text data, making applications like image captioning, visual question answering, and multi-modal content creation possible. This ability to understand and work with complex information has made MLLMs useful in areas such as healthcare, autonomous systems, and digital content. However, integrating multiple types of data also creates security risks. Attackers can manipulate either the visual or text inputs, or both, to make the model produce unintended or even harmful responses. This paper reviews how visual inputs in MLLMs can be exploited by various attack strategies. We break down these attacks into categories: simple visual tweaks and cross-modal manipulations, as well as advanced strategies like VLATTACK, HADES, and Collaborative Multimodal Adversarial Attack (Co-Attack). These attacks can mislead even the most robust models while looking nearly identical to the original visuals, making them hard to detect. We also discuss the broader security risks, including threats to privacy and safety in important applications. To counter these risks, we review current defense methods like the SmoothVLM framework, pixel-wise randomization, and MirrorCheck, looking at their strengths and limitations. We also discuss new methods to make MLLMs more secure, including adaptive defenses, better evaluation tools, and security approaches that protect both visual and text data. By bringing together recent developments and identifying key areas for improvement, this review aims to support the creation of more secure and reliable multi-modal AI systems for real-world use.