Text-to-image (T2I) models have shown remarkable progress, but their potential to generate harmful content remains a critical concern in the ML community. While various safety mechanisms have been developed, the field lacks systematic tools for evaluating their effectiveness against real-world misuse scenarios. In this work, we propose ICER, a novel red-teaming framework that leverages Large Language Models (LLMs) and a bandit optimization-based algorithm to generate interpretable and semantic meaningful problematic prompts by learning from past successful red-teaming attempts. Our ICER efficiently probes safety mechanisms across different T2I models without requiring internal access or additional training, making it broadly applicable to deployed systems. Through extensive experiments, we demonstrate that ICER significantly outperforms existing prompt attack methods in identifying model vulnerabilities while maintaining high semantic similarity with intended content. By uncovering that successful jailbreaking instances can systematically facilitate the discovery of new vulnerabilities, our work provides crucial insights for developing more robust safety mechanisms in T2I systems.

The recent development of Video-based Large Language Models (VideoLLMs), has significantly advanced video summarization by aligning video features and, in some cases, audio features with Large Language Models (LLMs). Each of these VideoLLMs possesses unique strengths and weaknesses. Many recent methods have required extensive fine-tuning to overcome the limitations of these models, which can be resource-intensive. In this work, we observe that the strengths of one VideoLLM can complement the weaknesses of another. Leveraging this insight, we propose a novel video summarization framework inspired by the Mixture of Experts (MoE) paradigm, which operates as an inference-time algorithm without requiring any form of fine-tuning. Our approach integrates multiple VideoLLMs to generate comprehensive and coherent textual summaries. It effectively combines visual and audio content, provides detailed background descriptions, and excels at identifying keyframes, which enables more semantically meaningful retrieval compared to traditional computer vision approaches that rely solely on visual information, all without the need for additional fine-tuning. Moreover, the resulting summaries enhance performance in downstream tasks such as summary video generation, either through keyframe selection or in combination with text-to-image models. Our language-driven approach offers a semantically rich alternative to conventional methods and provides flexibility to incorporate newer VideoLLMs, enhancing adaptability and performance in video summarization tasks.

The recent renaissance of deep learning techniques has While image data starts to enjoy the simple-but-effective brought a magic leap to various fields, such as computer vision, self-supervised learning scheme built upon masking and natural language processing, and robotics. Learning self-reconstruction objective thanks to the introduction of from a large-scale labeled/supervised dataset, which is one tokenization procedure and vision transformer backbone, of the key factors leading to the success of deep learning, convolutional neural networks as another important and however, has now turned out to be a significant limitation widely-adopted architecture for image data, though having on its extensions to more fields. In addition to the expensive contrastive-learning techniques to drive the self-supervised cost of time and human resources to collect training learning, still face the difficulty of leveraging such straightforward datasets for different tasks and their corresponding labels, and general masking operation to benefit their the supervised learning scenario typically would suffer from learning process significantly. In this work, we aim to the issue of overfitting on the training dataset, thus leading alleviate the burden of including masking operation into to worse generalizability of the learnt models. These problems the contrastive-learning framework for convolutional neural bring challenges for the application of deep learning networks as an extra augmentation method. In addition techniques but also give rise to the research topic of selfsupervised to the additive but unwanted edges (between masked and learning, wherein it aims to learn to extract informative unmasked regions) as well as other adverse effects caused feature representations from an unlabelled dataset by the masking operations for ConvNets, which have been via leveraging the underlying structure of data and building discussed by prior works, we particularly identify the potential the supervisory signals from the data itself. The discovered problem where for one view in a contrastive samplepair representations are typically more general and can be further the randomly-sampled masking regions could be overly utilized or fine-tuned to various downstream tasks.

Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the recent wave of transformative AI. Nevertheless, such advance comes with an intensifying concern about the misuse of this generative technology, especially for producing copyrighted or NSFW (i.e. not safe for work) images. Although efforts have been made to filter inappropriate images/prompts or remove undesirable concepts/styles via model fine-tuning, the reliability of these safety mechanisms against diversified problematic prompts remains largely unexplored. In this work, we propose Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism. We demonstrate the efficacy of our P4D tool in uncovering new vulnerabilities of SD models with safety mechanisms. Particularly, our result shows that around half of prompts in existing safe prompting benchmarks which were originally considered "safe" can actually be manipulated to bypass many deployed safety mechanisms, including concept removal, negative prompt, and safety guidance. Our findings suggest that, without comprehensive testing, the evaluations on limited safe prompting benchmarks can lead to a false sense of safety for text-to-image models.