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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Chuanhao Jing, Chenchen Li, Zhen Zhai, Mingliang Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China School of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798891761643

Recent work has revealed that in-context learning for large language models exhibits compositional generalization capacity, which can be enhanced by selecting in-context demonstrations similar to test cases to provide contextual information. However, how to exhibit in-context compositional generalization (ICCG) of large vision-language models (LVLMs) is non-trival. Due to the inherent asymmetry between visual and linguistic modalities, ICCG in LVLMs faces an inevitable challenge-redundant information on the visual modality. The redundant information affects in-context learning from two aspects: (1) Similarity calculation may be dominated by redundant information, resulting in sub-optimal demonstration selection. (2) Redundant information in in-context demonstrations brings misleading contextual information to in-context learning. To alleviate these problems, we propose a demonstration selection method to achieve ICCG for LVLMs, by considering two key factors of demonstrations: content and structure, from a multimodal perspective. Specifically, we design a diversity-coverage-based matching score to select demonstrations with maximum coverage, and avoid selecting demonstrations with redundant information via their content redundancy and structural complexity. We build a GQA-ICCG dataset to simulate the ICCG setting, and conduct experiments on GQA-ICCG and the VQA v2 dataset. Experimental results demonstrate the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Watershed-based weighted graph partitioning active contours

Watershed-based weighted graph partitioning active contours

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2010 3rd International Congress on Image and Signal Processing, CISP 2010

作者： Wang, Shaorong Chen, Yisong Li, Sheng Wang, Guoping Li, Hua Key Lab. of Machine Perception and Intelligent MOE Beijing 100871 China HCI and Multimedia Lab. Dept. of Computer Science Peking University Beijing 100871 China National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

A novel Weighted Graph Partitioning Active Contours method based on weighted dissimilarity is introduced. This method is easy to be extended by defining different types of similarities. And it has been greatly acceler... 详细信息

ISBN: (纸本)9781424465149

关键词： Image segmentation

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Forgery-aware Adaptive Learning with Vision Transformer for Generalized Face Forgery Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第5期35卷 4116-4129页

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Life, Alex C. Kot Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou510006 China Lab School of Electrical and Electronic Engineering Singapore Singapore Sun Yat-Sen University Guangdong Key Lab of Information Security School of Computer Science and Engineering Guangzhou510006 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen518116 China China-Singapore International Joint Research Institute Singapore Singapore

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. To this end, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm for generalized face forgery detection, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. The code and trained model have been released at: https://***/LoveSiameseCat/FAViT. © 1991-2012 IEEE.

关键词： Forgery Faces Adaptation models Transformers Feature extraction Adaptive learning Computer vision Face recognition Deepfakes Visualization

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DiRLoc: Disentanglement Representation Learning for Robust Image Forgery Localization

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IEEE Transactions on Dependable and Secure computing 2024年第03期22卷 2841-2854页

作者： Sheng, Ziqi Qu, Zuomin Lu, Wei Cao, Xiaochun Huang, Jiwu Sun Yat-sen University School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen518116 China

Deep Learning image forgery localization methods have achieved remarkable results but cannot maintain comparable performance when the forgery images are JPEG compressed, a format that is widely used in daily information transmission. The robustness against JPEG compression has become a bottleneck to the practical application of image forgery localization. To address this issue, a robust image forgery localization framework is proposed against the performance degradation caused by JPEG compression. Specifically, a cutting-edge progressive disentanglement strategy is proposed that incorporates coarse-grained image disentanglement to mitigate the detrimental effects of general JPEG compression, while harnessing the ability of fine-grained element disentanglement to separate multi-scale artifacts, thereby minimizing interference from content information. Moreover, the decision strategy is carefully designed to reinforce subtle signals from tampered areas, including artifacts fusion block reasoning multi-scale artifacts and dual attention block that learn more about forgery-related features. Extensive visualizations and experiments demonstrate that our method can achieve competitive performance in general JPEG-resistant image forgery localization, especially in the performance of generalization experiments. © 2004-2012 IEEE.

关键词： Image compression

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GAN-based Symmetric Embedding Costs Adjustment for Enhancing Image Steganographic Security 24

GAN-based Symmetric Embedding Costs Adjustment for Enhancing...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ye, Miaoxin Zhou, Saixing Luo, Weiqi Tan, Shunquan Huang, Jiwu Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou China Shenzhen Key Laboratory of Media Security College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798400706868

Designing embedding costs is pivotal in modern image steganography. Many studies have shown adjusting symmetric embedding costs to asymmetric ones can enhance steganographic security. However, most existing methods heavily depend on manually defined parameters or rules, limiting security performance improvements. To overcome this limitation, we introduce an advanced GAN-based framework that transitions symmetric costs to asymmetric ones without the need for the manual intervention seen in existing approaches, such as the detailed specification of cost modulation directions and magnitudes. In our framework, we firstly achieve symmetric costs for a cover image, which is randomly split into two sub-images, with part of the secret information embedded into one. Subsequently, we design a GAN model to adjust the embedding costs of the second sub-image to asymmetric, facilitating the secure embedding of the remaining secret information. To support our phased embedding approach, our GAN's discriminator incorporates two steganalyers with different tasks: distinguishing the generator's final output, i.e., the stego image, from both the input cover image and the partially embedded stego image, providing diverse guidance to the generator. In addition, we introduce a simple yet effective update strategy to ensure a stable training process. Comprehensive experiments demonstrate that our method significantly enhances security over existing symmetric steganography techniques, achieving state-of-the-art levels compared to other methods focused on embedding costs adjustments. Additionally, detailed ablation studies validate our approach's effectiveness. © 2024 ACM.

关键词： Steganography

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CMA: A Chromaticity Map Adapter for Robust Detection of Screen-Recapture Document Images

CMA: A Chromaticity Map Adapter for Robust Detection of Scre...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Changsheng Chen Liangwei Lin Yongqi Chen Bin Li Jishen Zeng Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Alibaba Group Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The rebroadcasting of screen-recaptured document images introduces a significant risk to the confidential docu-ments processed in government departments and commer-cial companies. However, detecting recaptured document images subjected to distortions from online social networks (OSNs) is challenging since the common forensics cues, such as moiré pattern, are weakened during transmission. In this work, we first devise a pixel-level distortion model of the screen-recaptured document image to identify the robust features of color artifacts. Then, we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples. Based on the prior understanding, we design a chromaticity map adapter (CMA) to efficiently extract the chromaticity map, and feed it into the transformer backbone as multi-modal prompt tokens. To evaluate the performance of the pro-posed method, we collect a recaptured office document im-age dataset with over 10K diverse samples. Experimental results demonstrate that the proposed CMA method outper-forms a SOTA approach (with RGB modality only), reducing the average EER from 26.82% to 16.78%. Robustness eval-uation shows that our method achieves 0.8688 and 0.7554 AUCs under samples with JPEG compression $(QF=70)$ and resolution as low as $534\times 503$ pixels.

关键词： Image color analysis Social networking (online) Forensics Splicing Transform coding Feature extraction Distortion

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Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning

arXiv

引用

arXiv 2024年

作者： Yang, Shuo Shang, Zirui Wang, Yongqi Deng, Derong Chen, Hongwei Cheng, Qiyuan Wu, Xinxiao Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China

This paper proposes a novel framework for multi-label image recognition without any training data, called data-free framework, which uses knowledge of pre-trained Large Language Model (LLM) to learn prompts to adapt pre-trained Vision-Language Model (VLM) like CLIP to multi-label classification. Through asking LLM by well-designed questions, we acquire comprehensive knowledge about characteristics and contexts of objects, which provides valuable text descriptions for learning prompts. Then we propose a hierarchical prompt learning method by taking the multi-label dependency into consideration, wherein a subset of category-specific prompt tokens are shared when the corresponding objects exhibit similar attributes or are more likely to co-occur. Benefiting from the remarkable alignment between visual and linguistic semantics of CLIP, the hierarchical prompts learned from text descriptions are applied to perform classification of images during inference. Our framework presents a new way to explore the synergies between multiple pre-trained models for novel category recognition. Extensive experiments on three public datasets (MS-COCO, VOC2007, and NUS-WIDE) demonstrate that our method achieves better results than the state-of-the-art methods, especially outperforming the zero-shot multi-label recognition methods by 4.7% in mAP on MS-COCO. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Fine-Grained Annotation for Face Anti-Spoofing

arXiv

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arXiv 2023年

作者： Chen, Xu Jia, Yunde Wu, Yuwei Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Face anti-spoofing plays a critical role in safeguarding facial recognition systems against presentation attacks. While existing deep learning methods show promising results, they still suffer from the lack of fine-grained annotations, which lead models to learn task-irrelevant or unfaithful features. In this paper, we propose a fine-grained annotation method for face anti-spoofing. Specifically, we first leverage the Segment Anything Model (SAM) to obtain pixel-wise segmentation masks by utilizing face landmarks as point prompts. The face landmarks provide segmentation semantics, which segments the face into regions. We then adopt these regions as masks and assemble them into three separate annotation maps: spoof, living, and background maps. Finally, we combine three separate maps into a three-channel map as annotations for model training. Furthermore, we introduce the Multi-Channel Region Exchange Augmentation (MCREA) to diversify training data and reduce overfitting. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches in both intra-dataset and cross-dataset evaluations. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Residual Hyperbolic Graph Convolution Networks

arXiv

引用

arXiv 2024年

作者： Xue, Yangkai Dai, Jindou Lu, Zhipeng Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smoothing may occur as the number of model layers increases, limiting the representation capabilities of most current HGCN models. In this paper, we propose residual hyperbolic graph convolutional networks (R-HGCNs) to address the over-smoothing problem. We introduce a hyperbolic residual connection function to overcome the over-smoothing problem, and also theoretically prove the effectiveness of the hyperbolic residual function. Moreover, we use product manifolds and HyperDrop to facilitate the R-HGCNs. The distinctive features of the R-HGCNs are as follows: (1) The hyperbolic residual connection preserves the initial node information in each layer and adds a hyperbolic identity mapping to prevent node features from being indistinguishable. (2) Product manifolds in R-HGCNs have been set up with different origin points in different components to facilitate the extraction of feature information from a wider range of perspectives, which enhances the representing capability of R-HGCNs. (3) HyperDrop adds multiplicative Gaussian noise into hyperbolic representations, such that perturbations can be added to alleviate the over-fitting problem without deconstructing the hyperbolic geometry. Experiment results demonstrate the effectiveness of R-HGCNs under various graph convolution layers and different structures of product manifolds. © 2024, CC BY.

关键词： Convolution

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Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks

Video Anomaly Detection via Sequentially Learning Multiple P...

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International Conference on Computer Vision (ICCV)

作者： Chenrui Shi Che Sun Yuwei Wu Yunde Jia Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Learning multiple pretext tasks is a popular approach to tackle the nonalignment problem in unsupervised video anomaly detection. However, the conventional learning method of simultaneously learning multiple pretext tasks, is prone to sub-optimal solutions, incurring sharp performance drops. In this paper, we propose to sequentially learn multiple pretext tasks according to their difficulties in an ascending manner to improve the performance of anomaly detection. The core idea is to relax the learning objective by starting with easy pretext tasks in the early stage and gradually refine it by involving more challenging pretext tasks later on. In this way, our method is able to reduce the difficulties of learning and avoid converging to sub-optimal solutions. Specifically, we design a tailored sequential learning order for three widely-used pretext tasks. It starts with frame prediction task, then moves on to frame reconstruction task and last ends with frame-order classification task. We further introduce a new contrastive loss which makes the learned representations of normality more discriminative by pushing normal and pseudo-abnormal samples apart. Extensive experiments on three datasets demonstrate the effectiveness of our method.

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