Qixian Hao — accepted AI-conference papers

2025

DcDsDiff: Dual-Conditional and Dual-Stream Diffusion Model for Generative Image Tampering Localization

Qixian Hao, Shaozhang Niu, Jiwei Zhang, Kai Wang

IJCAI 2025

Generative Image Tampering (GIT), due to its high diversity and realism, poses a significant challenge to traditional image tampering localization techniques. Consequently, this paper introduces a denoising diffusion probabilistic model-based DcDsDiff, which comprises a Dual-View Conditional Network

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2025

InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models

Kai Wang, Shaozhang Niu, Qixian Hao, Jiwei Zhang

AAAI 2025technical

As artificial intelligence advances rapidly, particularly with the advent of GANs and diffusion models, the accuracy of Image Inpainting Localization (IIL) has become increasingly challenging. Current IIL methods face two main challenges: a tendency towards overconfidence, leading to incorrect predi…

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