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Lingyun Yu

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4 papers

1 author row

AAAI Conference 2025 Conference Paper

IDseq: Decoupled and Sequentially Detecting and Grounding Multi-Modal Media Manipulation

Runxin Liu
Tian Xie
Jiaming Li
Lingyun Yu
Hongtao Xie

Detecting and grounding multi-modal media manipulation aims to categorize the type and localize the region of manipulation for image-text pairs in both two modalities. Existing methods have not sufficiently explored the intrinsic properties of the manipulated images, which contain both forgery and content features, leading to inefficient utilization. To address this problem, we propose an Image-Driven Decoupled Sequential Framework (IDseq), designed to decouple image features and rationally integrate them to accomplish different sub-tasks effectively. Specifically, IDseq employs two specially designed disentangled losses to guide the disentangled learning of forgery and content features. To efficiently leverage these features, we propose a Decoupled Image Manipulation Decoder (DIMD) that processes image tasks within a decoupled schema. We mitigate their exclusive competition by separating the image tasks into forgery-relevant and content-relevant components and training them without gradient interaction. Additionally, we utilize content features enhanced by the proposed Manipulation Indicator Generator (MIG) for the text tasks, which provide the maximal visual information as a reference while eliminating interference from unverified image data. Extensive experiments show the superiority of our IDseq, where it notably outperforms SOTA methods on the fine-grained classification by 3.8% in mAP and the forgery face grounding by 8.7% in IoUmean, even 1.3% in F1 on the most challenging manipulated text grounding.

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NeurIPS Conference 2024 Conference Paper

ShowMaker: Creating High-Fidelity 2D Human Video via Fine-Grained Diffusion Modeling

Quanwei Yang
Jiazhi Guan
Kaisiyuan Wang
Lingyun Yu
Wenqing Chu
Hang Zhou
Zhiqiang Feng
Haocheng Feng

Although significant progress has been made in human video generation, most previous studies focus on either human facial animation or full-body animation, which cannot be directly applied to produce realistic conversational human videos with frequent hand gestures and various facial movements simultaneously. To address these limitations, we propose a 2D human video generation framework, named ShowMaker, capable of generating high-fidelity half-body conversational videos via fine-grained diffusion modeling. We leverage dual-stream diffusion models as the backbone of our framework and carefully design two novel components for crucial local regions (i. e. , hands and face) that can be easily integrated into our backbone. Specifically, to handle the challenging hand generation caused by sparse motion guidance, we propose a novel Key Point-based Fine-grained Hand Modeling module by amplifying positional information from raw hand key points and constructing a corresponding key point-based codebook. Moreover, to restore richer facial details in generated results, we introduce a Face Recapture module, which extracts facial texture features and global identity features from the aligned human face and integrates them into the diffusion process for face enhancement. Extensive quantitative and qualitative experiments demonstrate the superior visual quality and temporal consistency of our method.

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TIST Journal 2022 Journal Article

Dynamic-Aware Federated Learning for Face Forgery Video Detection

Ziheng Hu
Hongtao Xie
Lingyun Yu
Xingyu Gao
Zhihua Shang
Yongdong Zhang

The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be distributed on devices of different enterprises that cannot be centralized to share due to security and privacy restrictions. In this article, we propose a Federated Learning face forgery detection framework to train a global model collaboratively while keeping data on local devices. In order to make the detection model more robust, we propose a novel Inconsistency-Capture module (ICM) to capture the dynamic inconsistencies between adjacent frames of face forgery videos. The ICM contains two parallel branches. The first branch takes the whole face of adjacent frames as input to calculate a global inconsistency representation. The second branch focuses only on the inter-frame variation of critical regions to capture the local inconsistency. To the best of our knowledge, this is the first work to apply federated learning to face forgery video detection, which is trained with decentralized data. Extensive experiments show that the proposed framework achieves competitive performance compared with existing methods that are trained with centralized data, with higher-level security and privacy guarantee.

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AAAI Conference 2020 Conference Paper

Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization

Chuanbin Liu
Hongtao Xie
Zheng-Jun Zha
Lingfeng Ma
Lingyun Yu
Yongdong Zhang

Delicate attention of the discriminative regions plays a critical role in Fine-Grained Visual Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly in FGVC, due to the pivotal limitations in discriminative regions proposing and region-based feature learning. 1) The discriminative regions are predominantly located based on the ﬁlter responses over the images, which can not be directly optimized with a performance metric. 2) Existing methods train the region-based feature extractor as a one-hot classiﬁcation task individually, while neglecting the knowledge from the entire object. To address the above issues, in this paper, we propose a novel “Filtration and Distillation Learning” (FDL) model to enhance the region attention of discriminate parts for FGVC. Firstly, a Filtration Learning (FL) method is put forward for discriminative part regions proposing based on the matchability between proposing and predicting. Speciﬁcally, we utilize the proposing-predicting matchability as the performance metric of Region Proposal Network (RPN), thus enable a direct optimization of RPN to ﬁltrate most discriminative regions. Go in detail, the objectbased feature learning and region-based feature learning are formulated as “teacher” and “student”, which can furnish better supervision for region-based feature learning. Accordingly, our FDL can enhance the region attention effectively, and the overall framework can be trained end-to-end without neither object nor parts annotations. Extensive experiments verify that FDL yields state-of-the-art performance under the same backbone with the most competitive approaches on several FGVC tasks.

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