Author name cluster

Jianwei Fei

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

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.

PDF Details DOI

AAAI Conference 2025 Conference Paper

OmniMark: Efficient and Scalable Latent Diffusion Model Fingerprinting

Jianwei Fei
Yunshu Dai
Zhihua Xia
Fangjun Huang
Jiantao Zhou

We introduce OmniMark, a novel and efficient fingerprinting method for Latent Diffusion Models (LDM). OmniMark can encode user-specific fingerprints across diverse dimensions of the weights of the LDM, including kernels, filters, channels, and spatial domains. The LDM is fine-tuned to encode the invisible fingerprint into generated images, which can be decoded by a decoder. By altering fingerprints and re-encoding the weights, OmniMark supports efficient and scalable ad-hoc generation (<100 ms) of numerous models with unique fingerprints that enable user accountability and model attribution. Extensive experiments demonstrate that OmniMark can be applied to various image generation and editing tasks and achieve highly accurate fingerprint detection without compromising image quality. Furthermore, OmniMark demonstrates good robustness against both white-box model attacks and image attacks, including fine-tuning and JPEG compression.

PDF Details DOI

ICML Conference 2025 Conference Paper

Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability

Yunshu Dai
Jianwei Fei
Fangjun Huang
Chip Hong Chang

As AI generative models evolve, face swap technology has become increasingly accessible, raising concerns over potential misuse. Celebrities may be manipulated without consent, and ordinary individuals may fall victim to identity fraud. To address these threats, we propose Secure Swap, a method that protects persons of interest (POI) from face-swapping abuse and embeds a unique, invisible watermark into nonPOI swapped images for traceability. By introducing an ID Passport layer, Secure Swap redacts POI faces and generates watermarked outputs for nonPOI. A detachable watermark encoder and decoder are trained with the model to ensure provenance tracing. Experimental results demonstrate that Secure Swap not only preserves face swap functionality but also effectively prevents unauthorized swaps of POI and detects different embedded model’s watermarks with high accuracy. Specifically, our method achieves a 100% success rate in protecting POI and over 99% watermark extraction accuracy for nonPOI. Besides fidelity and effectiveness, the robustness of protected models against image-level and model-level attacks in both online and offline application scenarios is also experimentally demonstrated.

Details

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.

Details