ToLo: A Two-Stage, Training-Free Layout-To-Image Generation Framework For High-Overlap Layouts

Recent training-free layout-to-image diffusion models have demonstrated remarkable performance in generating high-quality images with controllable layouts. These models follow a one-stage framework: Encouraging the model to focus the attention map of each concept on its corresponding region by defining attention map-based losses. However, these models still struggle to accurately follow layouts with significant overlap, often leading to issues like attribute leakage and missing entities. In this paper, we propose ToLo, a two-stage, training-free layout-to-image generation framework for high-overlap layouts. Our framework consists of two stages: the aggregation stage and the separation stage, each with its own loss function based on the attention map. To provide a more effective evaluation, we partition the HRS dataset based on the Intersection over Union (IoU) of the input layouts, creating a new dataset for layout-to-image generation with varying levels of overlap. Through extensive experiments on this dataset, we demonstrate that ToLo significantly enhances the performance of existing methods when dealing with high-overlap layouts. Our code and dataset are available here: https://github.com/misaka12435/ToLo.

Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs

Video-based multimodal large language models (V-MLLMs) have shown vulnerability to adversarial examples in video-text multimodal tasks. However, the transferability of adversarial videos to unseen models--a common and practical real world scenario--remains unexplored. In this paper, we pioneer an investigation into the transferability of adversarial video samples across V-MLLMs. We find that existing adversarial attack methods face significant limitations when applied in black-box settings for V-MLLMs, which we attribute to the following shortcomings: (1) lacking generalization in perturbing video features, (2) focusing only on sparse key-frames, and (3) failing to integrate multimodal information. To address these limitations and deepen the understanding of V-MLLM vulnerabilities in black-box scenarios, we introduce the Image-to-Video MLLM (I2V-MLLM) attack. In I2V-MLLM, we utilize an image-based multimodal model (IMM) as a surrogate model to craft adversarial video samples. Multimodal interactions and temporal information are integrated to disrupt video representations within the latent space, improving adversarial transferability. In addition, a perturbation propagation technique is introduced to handle different unknown frame sampling strategies. Experimental results demonstrate that our method can generate adversarial examples that exhibit strong transferability across different V-MLLMs on multiple video-text multimodal tasks. Compared to white-box attacks on these models, our black-box attacks (using BLIP-2 as surrogate model) achieve competitive performance, with average attack success rates of 55.48% on MSVD-QA and 58.26% on MSRVTT-QA for VideoQA tasks, respectively. Our code will be released upon acceptance.