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Wenkui Yang, Jie Cao, Junxian Duan, Ran He

Diffusion models like Stable Diffusion have become prominent in visual synthesis tasks due to their powerful customization capabilities, which also introduce significant security risks, including deepfakes and copyright infringement. In response, a class of methods known as protective perturbation emerged, which mitigates image misuse by injecting imperceptible adversarial noise. However, purification can remove protective perturbations, thereby exposing images again to the risk of malicious forgery. In this work, we formalize the anti-purification task, highlighting challenges that hinder existing approaches, and propose a simple diagnostic protective perturbation named AntiPure. AntiPure exposes vulnerabilities of purification within the "purification-customization" workflow, owing to two guidance mechanisms: 1) Patch-wise Frequency Guidance, which reduces the model's influence over high-frequency components in the purified image, and 2) Erroneous Timestep Guidance, which disrupts the model's denoising strategy across different timesteps. With additional guidance, AntiPure embeds imperceptible perturbations that persist under representative purification settings, achieving effective post-customization distortion. Experiments show that, as a stress test for purification, AntiPure achieves minimal perceptual discrepancy and maximal distortion, outperforming other protective perturbation methods within the purification-customization workflow.

Xingshuo Han, Xuanye Zhang, Xiang Lan, Haozhao Wang, Shengmin Xu, Shen Ren, Jason Zeng, Ming Wu, Michael Heinrich, Tianwei Zhang

By using a control variate to calibrate the local gradient of each client, Scaffold has been widely known as a powerful solution to mitigate the impact of data heterogeneity in Federated Learning. Although Scaffold achieves significant performance improvements, we show that this superiority is at the cost of increased security vulnerabilities. Specifically, this paper presents BadSFL, the first backdoor attack targeting Scaffold, which turns benign clients into accomplices to amplify the attack effect. The core idea of BadSFL is to uniquely tamper with the control variate to subtly steer benign clients' local gradient updates towards the attacker's poisoned direction, effectively turning them into unwitting accomplices, significantly enhancing the backdoor persistence. Additionally, BadSFL leverages a GAN-enhanced poisoning strategy to enrich the attacker's dataset, maintaining high accuracy on both benign and backdoored samples while remaining stealthy. Extensive experiments demonstrate that BadSFL achieves superior attack durability, maintaining effectiveness for over 60 global rounds--lasting up to three times longer than existing baselines even after ceasing malicious model injections.

Xiao Li, Yiming Zhu, Yifan Huang, Wei Zhang, Yingzhe He, Jie Shi, Xiaolin Hu

Object detection plays a crucial role in many security-sensitive applications, such as autonomous driving and video surveillance. However, several recent studies have shown that object detectors can be easily fooled by physically realizable attacks, e.g., adversarial patches and recent adversarial textures, which pose realistic and urgent threats. Adversarial Training (AT) has been recognized as the most effective defense against adversarial attacks. While AT has been extensively studied in the l_infinity-bounded attack settings on classification models, AT against physically realizable attacks on object detectors has received limited exploration. Early attempts are only performed to defend against adversarial patches, leaving AT against a wider range of physically realizable attacks under-explored. In this work, we consider defending against various physically realizable attacks with a unified AT method. We propose PBCAT, a novel Patch-Based Composite Adversarial Training strategy. PBCAT optimizes the model by incorporating the combination of small-area gradient-guided adversarial patches and imperceptible global adversarial perturbations covering the entire image. With these designs, PBCAT has the potential to defend against not only adversarial patches but also unseen physically realizable attacks such as adversarial textures. Extensive experiments in multiple settings demonstrated that PBCAT significantly improved robustness against various physically realizable attacks over state-of-the-art defense methods. Notably, it improved the detection accuracy by 29.7% over previous defense methods under one recent adversarial texture attack.

Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera, Ruben Tolosana, Javier Ortega-Garcia

Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a novel multitask learning process that involves training simultaneously two models: the original or Audited Model, and a secondary model, referred to as the MINT Model, responsible for identifying the data used for training the Audited Model. This novel multi-task learning approach has been designed to incorporate the auditability of the model as an optimization objective during the training process of neural networks. The proposed approach incorporates intermediate activation maps as inputs to the MINT layers, which are trained to enhance the detection of training data. We present results using a wide range of neural networks, from lighter architectures such as MobileNet to more complex ones such as Vision Transformers, evaluated in 5 public benchmarks. Our proposed Active MINT achieves over 80% accuracy in detecting if given data was used for training, significantly outperforming previous approaches in the literature. Our aMINT and related methodological developments contribute to increasing transparency in AI models, facilitating stronger safeguards in AI deployments to achieve proper security, privacy, and copyright protection (Code available in https://github.com/DanieldeAlcala/Membership-Inference-Test.git)

Ziyin Zhou, Yunpeng Luo, Yuanchen Wu, Ke Sun, Jiayi Ji, Ke Yan, Shouhong Ding, Xiaoshuai Sun, Yunsheng Wu, Rongrong Ji

The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to public information security. Although existing AIGI detection techniques are generally effective, they face two issues: 1) a lack of human-verifiable explanations, and 2) a lack of generalization in the latest generation technology. To address these issues, we introduce a large-scale and comprehensive dataset, Holmes-Set, which includes the Holmes-SFTSet, an instruction-tuning dataset with explanations on whether images are AI-generated, and the Holmes-DPOSet, a human-aligned preference dataset. Our work introduces an efficient data annotation method called the Multi-Expert Jury, enhancing data generation through structured MLLM explanations and quality control via cross-model evaluation, expert defect filtering, and human preference modification. In addition, we propose Holmes Pipeline, a meticulously designed three-stage training framework comprising visual expert pre-training, supervised fine-tuning, and direct preference optimization. Holmes Pipeline adapts multimodal large language models (MLLMs) for AIGI detection while generating human-verifiable and human-aligned explanations, ultimately yielding our model AIGI-Holmes. During the inference stage, we introduce a collaborative decoding strategy that integrates the model perception of the visual expert with the semantic reasoning of MLLMs, further enhancing the generalization capabilities. Extensive experiments on three benchmarks validate the effectiveness of our AIGI-Holmes.

Ruofei Wang, Peiqi Duan, Boxin Shi, Renjie Wan

With more event datasets being released online, safeguarding the event dataset against unauthorized usage has become a serious concern for data owners. Unlearnable Examples are proposed to prevent the unauthorized exploitation of image datasets. However, it's unclear how to create unlearnable asynchronous event streams to prevent event misuse. In this work, we propose the first unlearnable event stream generation method to prevent unauthorized training from event datasets. A new form of asynchronous event error-minimizing noise is proposed to perturb event streams, tricking the unauthorized model into learning embedded noise instead of realistic features. To be compatible with the sparse event, a projection strategy is presented to sparsify the noise to render our unlearnable event streams (UEvs). Extensive experiments demonstrate that our method effectively protects event data from unauthorized exploitation, while preserving their utility for legitimate use. We hope our UEvs contribute to the advancement of secure and trustworthy event dataset sharing. Code is available at: https://github.com/rfww/uevs.

Fanjie Kong, Yitong Li, Weihuang Chen, Chen Min, Yizhe Li, Zhiqiang Gao, Haoyang Li, Zhongyu Guo, Hongbin Sun

The rise of embodied intelligence and multi-modal large language models has led to exciting advancements in the field of autonomous driving, establishing it as a prominent research focus in both academia and industry. However, when confronted with intricate and ambiguous traffic scenarios, the lack of logical reasoning and cognitive decision-making capabilities remains the primary challenge impeding the realization of embodied autonomous driving. Although Vision Language Models (VLMs) have enhanced the deep semantic understanding of autonomous driving systems, they exhibit notable limitations in decision explainability when handling rare and long-tail traffic scenarios. In this paper, we propose VLR-Driver, a novel multi-modal Vision-Language-Reasoning (VLR) framework based on Chain of Thought (CoT) for embodied autonomous driving. The framework employs a spatiotemporal CoT reasoning approach to recursively analyze potential safety risks and driving intentions of other agents, thereby delivering an efficient and transparent decision-making process. Furthermore, we construct a multi-modal reasoning-decision dataset to support the advancement of hierarchical reasoning of VLMs in autonomous driving. Closed-loop experiments conducted in CARLA demonstrate that the VLR-Driver significantly outperforms state-of-the-art end-to-end methods. Notably, key metrics such as driving score improved by 17.5%, while the success rate improved by 22.2%, offering a more transparent, reliable, and secure solution for autonomous driving systems.

Jinghan You, Shanglin Li, Yuanrui Sun, Jiangchuan Wei, Mingyu Guo, Chao Feng, Jiao Ran

Vision Transformers (ViTs) have revolutionized large-scale visual modeling, yet remain underexplored in face recognition (FR) where CNNs still dominate. We identify a critical bottleneck: CNN-inspired training paradigms fail to unlock ViT's potential, leading to suboptimal performance and convergence instability.To address this challenge, we propose LVFace, a ViT-based FR model that integrates Progressive Cluster Optimization (PCO) to achieve superior results. Specifically, PCO sequentially applies negative class sub-sampling (NCS) for robust and fast feature alignment from random initialization, feature expectation penalties for centroid stabilization, performing cluster boundary refinement through full-batch training without NCS constraints. LVFace establishes a new state-of-the-art face recognition baseline, surpassing leading approaches such as UniFace and TopoFR across multiple benchmarks. Extensive experiments demonstrate that LVFace delivers consistent performance gains, while exhibiting scalability to large-scale datasets and compatibility with mainstream VLMs and LLMs. Notably, LVFace secured 1st place in the ICCV 2021 Masked Face Recognition (MFR)-Ongoing Challenge (March 2025), proving its efficacy in real-world scenarios. Project is available at https://github.com/bytedance/LVFace.

Yasunori Mochizuki

NEC is the leading ICT technology provider in the B-to-B market and is actively integrating cutting-edge technologies into its business solutions to drive innovation, enhance capabilities, and create new value for its customers in a broad spectrum of industrial segments. And the recent business focus of NEC is to support digital transformation of business processes of customer enterprises by leveraging technical capabilities in AI, Cyber Security and Communication. This keynote discusses the specific role of NEC's Research in such a business context by sharing a variety of generative and multimodal AI-related use cases that are aimed at solving critical customer challenges in the real-world. From the multimedia perspective, the topics will include world-leading facial recognition technology for security boost and enhanced customer experience, development of drive-recorder video analytics for insurance adjusters leveraging visual language model (VLM) and medical document generation AI service for genuinely supporting overworked clinical doctors. Meanwhile, distributed acoustic sensing technology using optical fiber cables is opening a new opportunity for infrastructure and incident monitoring solutions after integration with AI and ML algorithms. As the common denominator, our commitment of solving critical customer challenges requires (and justifies) nurturing both world-class excellence in performing academic research and accumulated experience and/or culture of application-oriented technology refinement as well as technology combination to ensure business-ready practicality. Also, being the industrial research organization, we are engaged at the forefront of customer co-creation and co-design that play an indispensable role in pinpointing customer's critical challenges. These expertise and practices are indeed the core ingredients of NEC's Research for creating new business opportunities from the technology innovation approach. Furthermore, we also envision that such an industrial lab model in the Generative AI era will become the driver of a new technology paradigm - industry segment-oriented customizable foundation models and business transforming Agentic AI framework.

Sebastiano Battiato, Mirko Casu, Francesco Guarnera, Luca Guarnera, Giovanni Puglisi, Orazio Pontorno, Claudio Vittorio Ragaglia, Zahid Akhtar

The proliferation of generative models, particularly Generative Adversarial Networks (GANs) and Diffusion Models, has reshaped multimedia content creation. Alongside creative and commercial opportunities, they have introduced unprecedented risks through the production of highly realistic synthetic content, or deepfakes. These artifacts challenge visual and auditory trust, with major implications for media, security, politics, and law. This workshop provides a forum to examine deepfake technology from forensic, technical, legal, and social perspectives. It will bring together experts to advance robust and explainable detection methods, define benchmarking practices, and address ethical and regulatory frameworks. Topics include detection and attribution, adversarial countermeasures, multimodal analysis, model traceability, legal admissibility of synthetic content, as well as real-world deployment challenges and dataset creation. Further information about the workshop is available at https://iplab.dmi.unict.it/mfs/acm-dff-ws-2025/

Chuang Wang, Weidong Chen 0010, Xu Cui, Yiming Zhao, Zhaobo Qi, Pengqi Huang, Xinyan Liu 0008, Weigang Zhang

In contrast to traditional action recognition, Micro-Action Recognition focuses on identifying subtle, low-amplitude movements, which was constrained by two kinds of challenges. The first challenge is the spatial imbalance, where small, critical action regions are easily overwhelmed by vast, irrelevant backgrounds, leading to a low signal-to-noise ratio. The second challenge is the class distribution imbalance, where the natural occurrence of actions follows a long-tailed distribution, causing models to be biased towards common actions. To address these specific issues, our framework introduces two targeted solutions. To mitigate spatial imbalance, a YOLOv12-based detection module has been used to localize and crop salient body parts, forcing the model to focus on action-relevant regions. Concurrently, to mitigate class imbalance, this study implement a dynamic oversampling strategy combined with temporal data augmentation, effectively re-weighting the training process to improve performance on rare categories. Integrated with a V-JEPA2 backbone and a multi-classifier ensemble, our approach demonstrates its efficacy by securing second place in the ACM MM'25 Micro-Action Analysis Challenge with an F1-score of 76.98%.

Ronghao Lin, Shuai Shen, Weipeng Hu, Qiaolin He, Aolin Xiong, Li Huang, Haifeng Hu 0001, Yap-Peng Tan

Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved text-based ERG, challenges remain in handling multimodal emotional content and maintaining identity consistency. Thus, we propose E3RG, an Explicit Emotion-driven Empathetic Response Generation System based on multimodal LLMs which decomposes MERG task into three parts: multimodal empathy understanding, empathy memory retrieval, and multimodal response generation. By integrating advanced expressive speech and video generative models, E3RG delivers natural, emotionally rich, and identity-consistent responses without extra training. Experiments validate the superiority of our system on both zero-shot and few-shot settings, securing Top-1 position in the Avatar-based Multimodal Empathy Challenge on ACM MM'25. Our code is available at https://github.com/RH-Lin/E3RG.

Fangyuan Liu, Sirui Zhao, Kang Yin, Tong Xu 0001, Enhong Chen

Depression is an increasingly prevalent mental health issue worldwide, especially among the elderly, where effective early detection is crucial for timely intervention. While recent multimodal approaches demonstrate promise in leveraging visual and auditory cues for automatic depression recognition, existing methods often fail to extract fine-grained, depression-specific patterns from pre-extracted features and underutilize cross-modal interactions. To address these challenges, we propose DepFormer, a unified framework that incorporates a Bimodal Collaborative Transformer(BCT) for cross-modal representation learning and a personalized fusion module to enhance individual-specific modeling. The architecture comprises: (1) unimodal feature extraction, (2) bimodal collaborative representation learning via the BCT, (3) personalized feature fusion, and (4) final depression classification. Critically, the BCT employs symmetric bidirectional branches comprising an Audio-to-Video Transformer and a Video-to-Audio Transformer to enable mutual enhancement and complementary learning across modalities. Extensive experiments validate DepFormer's effectiveness, securing first place in the MPDD Challenge 2025 (Elderly Track), underscoring its strong practical potential.

Jia Li 0013, Yang Wang 0023, Wenhao Qian, Jialong Hu, Zhenzhen Hu 0004, Richang Hong, Meng Wang 0001

Interview performance assessment is essential for determining candidates' suitability for professional positions. To ensure holistic and fair evaluations, we propose a novel and comprehensive framework that explores ''365'' aspects of interview performance by integrating three modalities (video, audio, and text), six responses per candidate, and five key evaluation dimensions. The framework employs modality-specific feature extractors to encode heterogeneous data streams and subsequently fused via a Shared Compression Multilayer Perceptron. This module compresses multimodal embeddings into a unified latent space, facilitating efficient feature interaction. To enhance prediction robustness, we incorporate a two-level ensemble learning strategy: (1) independent regression heads predict scores for each response, and (2) predictions are aggregated across responses using a mean-pooling mechanism to produce final scores for the five target dimensions. By listening to the unspoken, our approach captures both explicit and implicit cues from multimodal data, enabling comprehensive and unbiased assessments. Achieving a multi-dimensional average MSE of 0.1824, our framework secured first place in the AVI Challenge 2025, demonstrating its effectiveness and robustness in advancing automated and multimodal interview performance assessment. The full implementation is available at https://github.com/Qianvenh/AVI2025-Track2.

Zhengqin Lai, Zhilin Zhu 0001, Xiaopeng Hong, Yaowei Wang 0001

This paper focuses on Open-Vocabulary Multimodal Emotion Recognition (OV-MER) and is dedicated to solving the two challenges it faces: concept semantic misalignment and incomplete coverage of fine-grained emotion categories. To address this, we propose a novel cognitive agent framework (Agent-MER), which reframes the OV-MER task as a problem to be solved by an agent that mimics the human cognitive process through knowledge-guided deliberation. We first construct a hierarchical Emotion Tree to serve as the agent's knowledge base. Building on this, we design a Knowledge-Guided Hierarchical Deliberation reasoning process. This process systematically explores the entire emotional landscape through a three-level, coarse-to-fine iterative reasoning process, enabling the identification of a richer and deeper range of emotions. Finally, a Self-Consistent Voting mechanism is employed to aggregate the results from multiple reasoning runs, ensuring the robustness of the final output. Experiments conducted in the MER2025 Challenge demonstrate that our proposed method achieved a top-ranking score of 61.04%, securing first place and significantly outperforming existing baselines. This work not only provides an effective solution for OV-MER but also opens up new avenues for developing more human-like affective intelligence systems.

Yuesheng Huang, Jinming Liu, Jiajia Chen, Yihang Lin, Yanmei Chen, Jianwei Dong

Multimodal Emotion Recognition (MER) has advanced significantly with the advent of Multimodal Large Language Models (MLLMs), which enable generative, descriptive understanding of complex human affect. However, the inherent ''black-box'' nature of these end-to-end models limits their trustworthiness and applicability in high-stakes domains, particularly due to their opacity in handling conflicting cross-modal cues (e.g., sarcasm). To address this critical gap, we propose Affective-CoT, a novel hierarchical framework that explicitly decouples perception from reasoning to achieve interpretable and faithful emotion analysis. Our framework utilizes specialized perception models to extract structured semantic evidence from raw audiovisual streams, which is then integrated and arbitrated by a central reasoning LLM executing a meticulously designed Cognitive Workflow. Critically, Affective-CoT generates a nuanced emotion description grounded in a transparent, human-interpretable reasoning trace. The efficacy of our framework was decisively validated by securing first place in the official MER-2025 Descriptive Emotion Understanding (DES) challenge. This result not only highlights the superiority of our method but also champions a new paradigm for building scrutable and trustworthy affective computing systems.

Jun Yu 0001, Xilong Lu, Cong Wang 0039, Qiang Ling 0001

Multimodal interleaved reasoning, which requires models to understand interleaved image-text sequences and multiple images, is a critical challenge in contemporary AI. This paper proposes a parameter-efficient fine-tuning framework based on Large Vision-Language Models, with Qwen2.5-VL as the backbone and Low-Rank Adaptation for task-specific adaptation. The framework integrates four stages: multimodal input preprocessing to align with pre-training distributions, visual feature extraction via a modified Vision Transformer, cross-modal fusion via attention mechanisms, and response generation via an autoregressive decoder. By freezing pre-trained weights and fine-tuning low-rank adapters in both visual and language modules, it balances preserving general multimodal knowledge with optimizing target tasks, achieving high performance with low computational overhead. On the MIRAGE Challenge Track A Dataset, it performs strongly across subtasks, achieving an aggregate score of 0.7857 and securing second place in the challenge. Ablation studies confirm that joint LoRA fine-tuning of visual and language modules yields optimal results; limitations in fine-grained visual difference tasks indicate future directions in enhancing subtle feature capture and adaptive cross-modal alignment.

Yuji Wang, Moran Li, Xiaobin Hu, Ran Yi 0002, Jiangning Zhang, Han Feng, Weijian Cao, Yabiao Wang, Chengjie Wang 0001, Lizhuang Ma

Identity-preserving text-to-video (IPT2V) generation, which aims to create high-fidelity videos with consistent human identity, has become crucial for downstream applications. However, current end-to-end frameworks suffer a critical spatial-temporal trade-off: optimizing for spatially coherent layouts of key elements ( e.g., character identity preservation) often compromises instruction-compliant temporal smoothness, while prioritizing dynamic realism risks disrupting the spatial coherence of visual structures. To tackle this issue, we propose a simple yet effective spatial-temporal decoupled framework that decomposes representations into spatial features for layouts and temporal features for motion dynamics. Specifically, our paper proposes a semantic prompt optimization mechanism and stage-wise decoupled generation paradigm. The former module decouples the prompt into spatial and temporal components. Aligned with the subsequent stage-wise decoupled approach, the spatial prompts guide the text-to-image (T2I) stage to generate coherent spatial features, while the temporal prompts direct the sequential image-to-video (I2V) stage to ensure motion consistency. Experimental results validate that our approach achieves excellent spatiotemporal consistency, demonstrating outstanding performance in identity preservation, text relevance, and video quality. By leveraging this simple yet robust mechanism, our algorithm secures the runner-up position in 2025 ACM Multimedia Challenge. Our code is available at https://github.com/rain152/IPVG.

Huiming Zheng, Wei Gao 0003

Amidst the swift advancement of 3D vision technology, Multi-view Compression (MVC) has become a crucial technique, widely applied in fields such as virtual reality, augmented reality, autonomous driving, telemedicine, and security surveillance. The technology effectively handles views from multiple cameras, utilizing the inter-view correlations to compress data efficiently. It substantially decreases the data transmission and storage requirements, enabling a richer and more realistic visual experience within the same bandwidth constraints. To further enhance compression performance, new methods continue to emerge. However, the absence of a unified benchmark testing library capable of effectively evaluating existing algorithms poses significant challenges to the further development of the field and the practical deployment of algorithms. To address this issue, we introduce OpenMVC, an Open-Source Library for Learning-based Multi-view Compression. We provide a comprehensive description and analysis of the performance advantages of existing algorithms. Furthermore, we conduct extensive and comprehensive benchmark testing of nine typical algorithms in the last five years, evaluating them in a consistent environment across various metrics. The open-source library for OpenMVC is available at https://openi.pcl.ac.cn/OpenAICoding/OpenMVC.

Changtao Miao, Yi Zhang, Man Luo, Weiwei Feng, Kaiyuan Zheng, Qi Chu 0001, Tao Gong, Jianshu Li, Yunfeng Diao, Wei Zhou 0021 等

Rapid advances in Artificial Intelligence Generated Content (AIGC) have enabled increasingly sophisticated face forgeries, posing a significant threat to social security. However, current Deepfake detection methods are limited by constraints in existing datasets, which lack the diversity necessary in real-world scenarios. Specifically, these data sets fall short in four key areas: unknown of advanced forgery techniques, variability of facial scenes, richness of real data, and degradation of real-world propagation. To address these challenges, we propose the Multi-dimensional Face Forgery Image (MFFI ) dataset, tailored for real-world scenarios. MFFI enhances realism based on four strategic dimensions: 1) Wider Forgery Methods; 2) Varied Facial Scenes; 3) Diversified Authentic Data; 4) Multi-level Degradation Operations. MFFI integrates 50 different forgery methods and contains 1024K image samples. Benchmark evaluations show that MFFI outperforms existing public datasets in terms of scene complexity, cross-domain generalization capability, and detection difficulty gradients. These results validate the technical advance and practical utility of MFFI in simulating real-world conditions. The dataset and additional details are publicly available at https://github.com/inclusionConf/MFFI.