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Chuangchuang Tan

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

ENHash: Error Notebook-Guided Fine-Grained Learning for Unsupervised Cross-Modal Hashing

Hao Fu
Zebing Yao
Chuangchuang Tan
Guanghua Gu

Without manual annotations, unsupervised cross-modal hashing (UCMH) aims to achieve efficient clustering and retrieval by leveraging data interrelationships. However, the retrieval accuracy is constrained by two main aspects: 1) insufficient exploration of data relationships; 2) existing knowledge mining strategies are not well aligned with the architectural properties of multilayer perceptrons. Through summary and error analysis, the human brain is able to achieve fast learning through experience and minimal data. Inspired by this cognitive process, we propose a novel Error Notebook strategy, named ENHash, to more effectively capture similarity information between multi-modal data for fine-grained unsupervised clustering. Firstly, simulating the human process of summarizing experiences, ENHash gradually integrates the information from each batch into a global clustering representation. Secondly, drawing upon human error analysis capabilities, ENHash utilizes the summarized experiences to identify and record incorrectly predicted hash codes. Finally, by leveraging the knowledge derived from this analysis, ENHash guides the hash function to learn fine-grained patterns from the errors. To the best of our knowledge, ENHash represents the first attempt at integrating cognitively-inspired mechanisms into fine-grained UCMH optimization paradigms. We evaluate the proposed ENHash against eight state-of-the-art methods on three widely used datasets and one fine-grained cross-modal dataset. Experimental results show that ENHash achieves substantial improvements over existing approaches.

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AAAI Conference 2026 Conference Paper

RAIN: Redundancy-Aware Latent Injection for Quality-Preserving Image Watermarking

Yehan Sun
Rongrong Ni
Chuangchuang Tan
Huan Liu
Wenhao Ni
Renshuai Tao
Yao Zhao

Diffusion models have gained widespread adoption due to their ability to generate highly realistic images, yet their rapid proliferation also raises security and traceability concerns. To address issues of ownership verification and accountability, current watermarking techniques primarily focus on embedding information into the internal mechanisms of generative pipelines. Nevertheless, many existing methods inject watermarks directly into latent representations without adequately exploiting inherent redundancies or perceptual properties in latent space, leading to degraded image quality. In this work, we conduct a systematic analysis aimed at quantifying differentiated redundancies present within latent space, and further propose a novel Redundancy-Aware Latent Injection framework RAIN based on the above analysis. Specifically, a redundancy‑aware adaptive watermark fusion method is introduced to preserve image quality, which utilizes the differentiated redundancy distribution to guide adaptive watermark allocation in different perception tolerance regions. Moreover, a distribution alignment initialization strategy is designed to align the watermark’s initial distribution to the latent prior, reducing initialization bias and improving convergence efficiency. Comprehensive experimental evaluations demonstrate that RAIN achieves state-of-the-art performance by delivering superior perceptual quality under high-capacity watermarking scenarios.

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AAAI Conference 2025 Conference Paper

C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection

Chuangchuang Tan
Renshuai Tao
Huan Liu
Guanghua Gu
Baoyuan Wu
Yao Zhao
Yunchao Wei

This work focuses on AIGC detection to develop universal detectors capable of identifying various types of forgery images. Recent studies have found large pre-trained models, such as CLIP, are effective for generalizable deepfake detection along with linear classifiers. However, two critical issues remain unresolved: 1) understanding why CLIP features are effective on deepfake detection through a linear classifier; and 2) exploring the detection potential of CLIP. In this study, we delve into the underlying mechanisms of CLIP's detection capabilities by decoding its detection features into text and performing word frequency analysis. Our finding indicates that CLIP detects deepfakes by recognizing similar concepts. Building on this insight, we introduce Category Common Prompt CLIP, called C2P-CLIP, which integrates the category common prompt into the text encoder to inject category-related concepts into the image encoder, thereby enhancing detection performance. Our method achieves a 12.4% improvement in detection accuracy compared to the original CLIP.

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NeurIPS Conference 2025 Conference Paper

DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing

Zixiang Li
Haoyu Wang
Wei Wang
Chuangchuang Tan
Yunchao Wei
Yao Zhao

Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise representation for a real or generated image, enabling reconstruction, editing, and other downstream tasks. However, to date, most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility. This limitation arises from the difficulty of maintaining both semantic alignment and structural consistency during the inversion process. In this work, we introduce Dual-Conditional Inversion (DCI), a novel framework that jointly conditions on the source prompt and reference image to guide the inversion process. Specifically, DCI formulates the inversion process as a dual-condition fixed-point optimization problem, minimizing both the latent noise gap and the reconstruction error under the joint guidance. This design anchors the inversion trajectory in both semantic and visual space, leading to more accurate and editable latent representations. Our novel setup brings new understanding to the inversion process. Extensive experiments demonstrate that DCI achieves state-of-the-art performance across multiple editing tasks, significantly improving both reconstruction quality and editing precision. Furthermore, we also demonstrate that our method achieves strong results in reconstruction tasks, implying a degree of robustness and generalizability approaching the ultimate goal of the inversion process. Our codes are available at: https: //github. com/Lzxhh/Dual-Conditional-Inversion

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AAAI Conference 2025 Conference Paper

ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks

Renshuai Tao
Manyi Le
Chuangchuang Tan
Huan Liu
Haotong Qin
Yao Zhao

Despite significant advances in deepfake detection, handling varying image quality, especially due to different compressions on online social networks (OSNs), remains challenging. Current methods succeed by leveraging correlations between paired images, whether raw or compressed. However, in open-world scenarios, paired data is scarce, with compressed images readily available but corresponding raw versions difficult to obtain. This imbalance, where unpaired data vastly outnumbers paired data, often leads to reduced detection performance, as existing methods struggle without corresponding raw images. To overcome this issue, we propose a novel approach named the open-world deepfake detection network (ODDN), which comprises two core modules: open-world data aggregation (ODA) and compression-discard gradient correction (CGC). ODA effectively aggregates correlations between compressed and raw samples through both fine-grained and coarse-grained analyses for paired and unpaired data, respectively. CGC incorporates a compression-discard gradient correction to further enhance performance across diverse compression methods in OSN. This technique optimizes the training gradient to ensure the model remains insensitive to compression variations. Extensive experiments conducted on 17 popular deepfake datasets demonstrate the superiority of the ODDN over SOTA baselines.

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IJCAI Conference 2025 Conference Paper

Unlocking the Potential of Lightweight Quantized Models for Deepfake Detection

Renshuai Tao
Ziheng Qin
Yifu Ding
Chuangchuang Tan
Jiakai Wang
Wei Wang

Deepfake detection is increasingly crucial due to the rapid rise of AI-generated content. Existing methods achieve high performance relying on computationally intensive large models, making real-time detection on resource-constrained edge devices challenging. Given that deepfake detection is a binary classification task, there is potential for model compression and acceleration. In this paper, we propose a low-bit quantization framework for lightweight and efficient deepfake detection. The Connected Quantized Block extracts common forgery features via the quantized path and retains method-specific textures through the shortcut connections. Additionally, the Shifted Logarithmic Redistribution Quantizer mitigates information loss in near-zero domains by unfolding the unbalanced activations, enabling finer quantization granularity. Comprehensive experiments demonstrate that this new framework significantly reduces 10. 8x computational costs and 12. 4x storage requirements while maintaining high detection performance, even surpassing SOTA methods using less than 5% FLOPs, paving the way for efficient deepfake detection in resource-limited scenarios.

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AAAI Conference 2024 Conference Paper

Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

Chuangchuang Tan
Yao Zhao
Shikui Wei
Guanghua Gu
Ping Liu
Yunchao Wei

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8\%) while requiring fewer parameters. The code is available at https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection.

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