290 citations · 1k across the 19 of their papers we have counts for
19 papers
NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild
Aleksandr Gushchin, Khaled Abud, Ekaterina Shumitskaya +51
This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal o…
SDiFL: Stable Diffusion-Driven Framework for Image Forgery Localization
Yang Su, Shunquan Tan, Jiwu Huang
Driven by the new generation of multi-modal large models, such as Stable Diffusion (SD), image manipulation technologies have advanced rapidly, posing significant challenges to ima…
ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack
Rongxuan Peng, Shunquan Tan, Chenqi Kong +3
Parameter-efficient fine-tuning (PEFT) has emerged as a popular strategy for adapting large vision foundation models, such as the Segment Anything Model (SAM) and LLaVA, to downstr…
CLUE: Leveraging Low-Rank Adaptation to Capture Latent Uncovered Evidence for Image Forgery Localization
Youqi Wang, Shunquan Tan, Rongxuan Peng +2
The increasing accessibility of image editing tools and generative AI has led to a proliferation of visually convincing forgeries, compromising the authenticity of digital media. I…
Active Adversarial Noise Suppression for Image Forgery Localization
Rongxuan Peng, Shunquan Tan, Xianbo Mo +2
Recent advances in deep learning have significantly propelled the development of image forgery localization. However, existing models remain highly vulnerable to adversarial attack…
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
Pei Yan, Shunquan Tan, Miaohui Wang +1
Dynamic analysis methods effectively identify shelled, wrapped, or obfuscated malware, thereby preventing them from invading computers. As a significant representation of dynamic m…
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization
Long Zhuo, Shenghai Luo, Shunquan Tan +3
Anti-forensics seeks to eliminate or conceal traces of tampering artifacts. Typically, anti-forensic methods are designed to deceive binary detectors and persuade them to misjudge…
STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition
Shunquan Tan, Qiushi Li, Laiyuan Li +2
Recent studies shows that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. The e…
Universal Deep Network for Steganalysis of Color Image based on Channel Representation
Kangkang Wei, Weiqi Luo, Shunquan Tan +1
Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this…
Self-Adversarial Training incorporating Forgery Attention for Image Forgery Localization
Long Zhuo, Shunquan Tan, Bin Li +1
Image editing techniques enable people to modify the content of an image without leaving visual traces and thus may cause serious security risks. Hence the detection and localizati…
MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography
Xianbo Mo, Shunquan Tan, Bin Li +1
Recent research has shown that non-additive image steganographic frameworks effectively improve security performance through adjusting distortion distribution. However, as far as w…
Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image
Xinghong Qin, Shunquan Tan, Bin Li +2
Nowadays a steganography has to face challenges of both feature based staganalysis and convolutional neural network (CNN) based steganalysis. In this paper, we present a novel steg…
CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images
Shunquan Tan, Weilong Wu, Zilong Shao +3
Over the past few years, detection performance improvements of deep-learning based steganalyzers have been usually achieved through structure expansion. However, excessive expanded…
Identification of Deep Network Generated Images Using Disparities in Color Components
Haodong Li, Bin Li, Shunquan Tan +1
With the powerful deep network architectures, such as generative adversarial networks, one can easily generate photorealistic images. Although the generated images are not dedicate…
CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography
Weixuan Tang, Bin Li, Shunquan Tan +2
Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a…
WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images
Jishen Zeng, Shunquan Tan, Guangqing Liu +2
Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for…
A multi-branch convolutional neural network for detecting double JPEG compression
Bin Li, Hu Luo, Haoxin Zhang +2
Detection of double JPEG compression is important to forensics analysis. A few methods were proposed based on convolutional neural networks (CNNs). These methods only accept inputs…
Large-scale JPEG steganalysis using hybrid deep-learning framework
Jishen Zeng, Shunquan Tan, Bin Li +1
Adoption of deep learning in image steganalysis is still in its initial stage. In this paper we propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating…
JPEG Noises beyond the First Compression Cycle
Bin Li, Tian-Tsong Ng, Xiaolong Li +2
This paper focuses on the JPEG noises, which include the quantization noise and the rounding noise, during a JPEG compression cycle. The JPEG noises in the first compression cycle…