Author name cluster

Yijun Bei

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5 papers

2 author rows

IJCAI Conference 2025 Conference Paper

CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection

Binjia Zhou
Hengrui Lou
Lizhe Chen
Haoyuan Li
Dawei Luo
Shuai Chen
Jie Lei
Zunlei Feng

With the swift progression of image generation technology, the widespread emergence of facial deepfakes poses significant challenges to the field of security, thus amplifying the urgent need for effective deepfake detection. Existing techniques for face forgery detection can broadly be categorized into two primary groups: visual-based methods and multimodal approaches. The former often lacks clear explanations for forgery details, while the latter, which merges visual and linguistic modalities, is more prone to the issue of hallucinations. To address these shortcomings, we introduce a visual detail enhanced self-correction framework, designated CorrDetail, for interpretable face forgery detection. CorrDetail is meticulously designed to rectify authentic forgery details when provided with error-guided questioning, with the aim of fostering the ability to uncover forgery details rather than yielding hallucinated responses. Additionally, to bolster the reliability of its findings, a visual fine-grained detail enhancement module is incorporated, supplying CorrDetail with more precise visual forgery details. Ultimately, a fusion decision strategy is devised to further augment the model's discriminative capacity in handling extreme samples, through the integration of visual information compensation and model bias reduction. Experimental results demonstrate that CorrDetail not only achieves state-of-the-art performance compared to the latest methodologies but also excels in accurately identifying forged details, all while exhibiting robust generalization capabilities.

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ICML Conference 2025 Conference Paper

STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification

Hengrui Lou
Zunlei Feng
Jinsong Geng
Erteng Liu
Jie Lei 0002
Lechao Cheng
Jie Song 0011
Mingli Song

With the rise of AIGC technologies, particularly diffusion models, highly realistic fake images that can deceive human visual perception has become feasible. Consequently, various forgery detection methods have emerged. However, existing methods treat the generation process of fake images as either a black-box or an auxiliary tool, offering limited insights into its underlying mechanisms. In this paper, we propose Spatio-Temporal Distribution Fitting Deviation (STD-FD) for AIGC forgery detection, which explores the generative process in detail. By decomposing and reconstructing data within generative diffusion models, initial experiments reveal temporal distribution fitting deviations during the image reconstruction process. These deviations are captured through reconstruction noise maps for each spatial semantic unit, derived via a super-resolution algorithm. Critical discriminative patterns, termed DFactors, are identified through statistical modeling of these deviations. Extensive experiments show that STD-FD effectively captures distribution patterns in AIGC-generated data, demonstrating strong robustness and generalizability while outperforming state-of-the-art (SOTA) methods on major datasets. The source code is available at this link.

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IJCAI Conference 2024 Conference Paper

Discriminative Feature Decoupling Enhancement for Speech Forgery Detection

Yijun Bei
Xing Zhou
Erteng Liu
Yang Gao
Sen Lin
Kewei Gao
Zunlei Feng

The emergence of AIGC has brought attention to the issue of generating realistic deceptive content. While AIGC has the potential to revolutionize content creation, it also facilitates criminal activities. Specifically, the manipulation of speech has been exploited in tele-fraud and financial fraud schemes, posing a significant threat to societal security. Current deep learning-based methods for detecting forged speech extract mixed features from the original speech, which often contain redundant information. Moreover, these methods fail to consider the distinct characteristics of human voice-specific features and the diversity of background environmental sounds. This paper introduces a framework called Discriminative fEature dEcoupling enhanceMent (DEEM) for detecting speech forgery. Initially, the framework decouples the original speech into human voice features and background sound features. Subsequently, DEEM enhances voice-specific features through temporal dimension aggregation and improves continuity-related features in the background sound map via spectral-dimension aggregation. By employing the decoupling enhancement features, extensive experiments demonstrate that DEEM achieves an accuracy improvement of over 5% on FoR dataset compared to the state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

Hundredfold Accelerating for Pathological Images Diagnosis and Prognosis through Self-reform Critical Region Focusing

XiaoTian Yu
Haoming Luo
Jiacong Hu
Xiuming Zhang
Yuexuan Wang
Wenjie Liang
Yijun Bei
Mingli Song

Pathological slides are commonly gigapixel images with abundant information and are therefore significant for clinical diagnosis. However, the ultra-large size makes both training and evaluation extremely time-consuming. Most existing methods need to crop the slide into patches, which also leads to large memory requirements. In this paper, we propose the Self-reform Multilayer Transformer (SMT) to accelerate the pathological image diagnosis and prognosis. Inspired by the pathologists' diagnostic procedure, SMT is designed to achieve layer-by-layer focus on critical regions. In the forward process, the first layer takes thumbnails as inputs and measures the significance of each patch that deserves focusing. Images from focused regions are cropped with a higher magnification and used as the input of the next layer. By analogy, the third layer inputs are focused images of second layer, which contain abundant cellular features. In addition to the forward focusing, the backward reform strategy is proposed to improve the precision of former layers. This cyclic process achieves iterative interactions for better performance on both classification and focusing. In this way, only a small part of critical patches are required in SMT for diagnosis and prognosis. Sufficient experiments demonstrate that SMT achieves hundreds times faster speed, while achieving comparable accuracy and less storage compared with existing SOTA methods.

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AAAI Conference 2024 Conference Paper

ViT-Calibrator: Decision Stream Calibration for Vision Transformer

Lin Chen
Zhijie Jia
Lechao Cheng
Yang Gao
Jie Lei
Yijun Bei
Zunlei Feng

A surge of interest has emerged in utilizing Transformers in diverse vision tasks owing to its formidable performance. However, existing approaches primarily focus on optimizing internal model architecture designs that often entail significant trial and error with high burdens. In this work, we propose a new paradigm dubbed Decision Stream Calibration that boosts the performance of general Vision Transformers. To achieve this, we shed light on the information propagation mechanism in the learning procedure by exploring the correlation between different tokens and the relevance coefficient of multiple dimensions. Upon further analysis, it was discovered that 1) the final decision is associated with tokens of foreground targets, while token features of foreground target will be transmitted into the next layer as much as possible, and the useless token features of background area will be eliminated gradually in the forward propagation. 2) Each category is solely associated with specific sparse dimensions in the tokens. Based on the discoveries mentioned above, we designed a two-stage calibration scheme, namely ViT-Calibrator, including token propagation calibration stage and dimension propagation calibration stage. Extensive experiments on commonly used datasets show that the proposed approach can achieve promising results.

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