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Peipeng Yu

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

CLIP-FTI: Fine-Grained Face Template Inversion via CLIP-Driven Attribute Conditioning

Longchen Dai
Zixuan Shen
Zhiheng Zhou
Peipeng Yu
Zhihua Xia

Face recognition systems store face templates for efficient matching. Once leaked, these templates pose a threat: inverting them can yield photorealistic surrogates that compromise privacy and enable impersonation. Although existing research has achieved relatively realistic face template inversion, the reconstructed facial images exhibit over-smoothed facial-part attributes (eyes, nose, mouth) and limited transferability. To address this problem, we present CLIP-FTI, a CLIP-driven fine-grained attribute conditioning framework for face template inversion. Our core idea is to use the CLIP model to obtain the semantic embeddings of facial features, in order to realize the reconstruction of specific facial feature attributes. Specifically, facial feature attribute embeddings extracted from CLIP are fused with the leaked template via a cross-modal feature interaction network and projected into the intermediate latent space of a pretrained Style- GAN. The StyleGAN generator then synthesizes face images with the same identity as the templates but with more finegrained facial feature attributes. Experiments across multiple face recognition backbones and datasets show that our reconstructions (i) achieve higher identification accuracy and attribute similarity, (ii) recover sharper component-level attribute semantics, and (iii) improve cross-model attack transferability compared to prior reconstruction attacks. To the best of our knowledge, ours is the first method to use additional information besides the face template attack to realize face template inversion and obtains SOTA results.

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AAAI Conference 2026 Conference Paper

Fine-Grained DINO Tuning with Dual Supervision for Face Forgery Detection

Tianxiang Zhang
Peipeng Yu
Zhihua Xia
Longchen Dai
Xiaoyu Zhou
Hui Gao

The proliferation of sophisticated deepfakes poses significant threats to information integrity. While DINOv2 shows promise for detection, existing fine-tuning approaches treat it as generic binary classification, overlooking distinct artifacts inherent to different deepfake methods. To address this, we propose a DeepFake Fine-Grained Adapter (DFF-Adapter) for DINOv2. Our method incorporates lightweight multi-head LoRA modules into every transformer block, enabling efficient backbone adaptation. DFF-Adapter simultaneously addresses authenticity detection and fine-grained manipulation type classification, where classifying forgery methods enhances artifact sensitivity. We introduce a shared branch propagating fine-grained manipulation cues to the authenticity head. This enables multi-task cooperative optimization, explicitly enhancing authenticity discrimination with manipulation-specific knowledge. Utilizing only 3.5M trainable parameters, our parameter-efficient approach achieves detection accuracy comparable to or even surpassing that of current complex state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

One for All: Synthesis-Free Fingerprint Learning for Attribution of In-the-Wild Synthetic Images

Jianwei Fei
Yunshu Dai
Peipeng Yu
Zhihua Xia
Dasara Shullani
Daniele Baracchi
Alessandro Piva

Attributing synthetic images to their source generative models is critical for digital forensics and security. While most existing attribution methods can distinguish images produced by known models and reject those from unknown ones, they are unable to verify whether a given image was produced by a specific, previously unseen model. To address this limitation, we formulate an open-set verification problem: determining whether a given image was generated by a specific model. Our key insight is that synthetic images from different models show consistent, content-independent fingerprints in their amplitude spectrum. Based on this insight, we design a dynamic fingerprint simulator capable of simulating over 1.6 trillion generative model architectures. We further train an extractor to capture model-specific fingerprint representations with supervised contrastive learning, enabling accurate attribution of synthetic images, even from previously unseen models. Our method does not rely on any synthetic images, instead, it is trained solely on real images. On DMDetection and AIGCBenchmark, which comprises dozens of state-of-the-art and in-the-wild generative models, our method improves the attribution performance (AUC) of the prior method from random level to 94.05% and 83.05%, respectively. On GenImage and OSMA datasets, we obtain 85.08%, and 88.48% OSCR, outperforming the SOTA methods by 4.30% and 9.37% under the same settings.

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ICML Conference 2025 Conference Paper

Unlocking the Capabilities of Large Vision-Language Models for Generalizable and Explainable Deepfake Detection

Peipeng Yu
Jianwei Fei
Hui Gao
Xuan Feng 0002
Zhihua Xia
Chip-Hong Chang

Current Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in understanding multimodal data, but their potential remains underexplored for deepfake detection due to the misalignment of their knowledge and forensics patterns. To this end, we present a novel framework that unlocks LVLMs’ potential capabilities for deepfake detection. Our framework includes a Knowledge-guided Forgery Detector (KFD), a Forgery Prompt Learner (FPL), and a Large Language Model (LLM). The KFD is used to calculate correlations between image features and pristine/deepfake image description embeddings, enabling forgery classification and localization. The outputs of the KFD are subsequently processed by the Forgery Prompt Learner to construct fine-grained forgery prompt embeddings. These embeddings, along with visual and question prompt embeddings, are fed into the LLM to generate textual detection responses. Extensive experiments on multiple benchmarks, including FF++, CDF2, DFD, DFDCP, DFDC, and DF40, demonstrate that our scheme surpasses state-of-the-art methods in generalization performance, while also supporting multi-turn dialogue capabilities.

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