I am currently an Associate Professor at the School of Computer Science and Technology, Xinjiang University, Urumqi, China. I received the Ph.D. degree in Computer Science and Technology from Hunan University in 2023, advised by Prof. Gaobo Yang. I am the PI of several projects, such as the Natural Science Foundation of China (NSFC). I have published several research papers in international journals, such as IEEE TIFS, TMM, TCSVT, and ACM TOMM. I am now leading the Visual Perception and Security Group (VPSG), with main research interests in multimedia forensics, image/video content security, visual scene understanding.

VPSG is looking for self-motivated students who are committed to research. Please feel free to contact Dr. Zhiqing Guo through the Email (attach your CV).

Homepage in Chinese

🔥 News

💰 Research Fundings, PI

• 2025.01-2028.12, “Research on Proactive Trusted Traceability Algorithm for Deepfake Faces”, National Natural Science Foundation of China (NSFC), Regional Project, Grant No.62462060
• 2024.01-2026.12, “Research on Image Content Security for AIGC”, Tianchi Talents Program of Autonomous Region
• 2024.01-2026.12, “Research on Deepfake Face Passive Forensics for Public Opinion Security”, National Natural Science Foundation of China (NSFC), Young Project, Grant No.62302427
• 2023.09-2026.08, “Research on Key Technologies of Face Image Authenticity Detection”, Autonomous Region Natural Science Foundation, Young Project, Grant No.2023D01C175
• 2023.03-2025.03, “Research on Efficient Passive Forensic Algorithm Based on Face Forgery Image”, Autonomous Region Education Department Project, Grant No.XJEDU2023P009

📝 Publications

NOTE: Corresponding Authors *, Equal Contribution #

🧙‍♂️ Deepfake Detection

AAAI 2026

Uncovering and Mitigating Destructive Multi-Embedding Attacks in Deepfake Proactive Forensics
L. Jia #, H. Sun #, Z. Guo*, Y. Diao, D. Ma, G. Yang. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026. (CCF-A) [Code]

• This work identifies the Multi-Embedding Attack (MEA) threat to deepfake watermarks and introduces Adversarial Interference Simulation (AIS), a plug-and-play training paradigm that equips existing methods with robust resistance against such attacks.

TCSVT 2025

WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks
Z. He, Z. Guo*, L. Wang, G. Yang, Y. Diao, and D. Ma*. IEEE Transactions on Circuits and Systems for Video Technology, 2025. (CCF-B, Top Journal) [Code]

• This work proposes a proactive watermarking framework, which explores frequency domain embedding and graph-based structural consistency optimization.

TIFS 2024

Constructing New Backbone Networks via Space-Frequency Interactive Convolution for Deepfake Detection
Z. Guo, Z. Jia*, L. Wang, D. Wang, G. Yang*, and N. Kasabov. IEEE Transactions on Information Forensics and Security, vol. 19, pp. 401-413, 2024, doi: 10.1109/TIFS.2023.3324739. (CCF-A, Top Journal) [Code]

• This work introduces a novel Space-Frequency Interactive Convolution (SFIConv) that seamlessly upgrades existing backbones to more effectively capture and model deepfake artifacts, significantly boosting detection accuracy and cross-dataset generalization.

TCSVT 2024

LDFnet: Lightweight Dynamic Fusion Network for Face Forgery Detection by Integrating Local Artifacts and Global Texture Information
Z. Guo, L. Wang*, W. Yang, G. Yang*, and K. Li. IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 2, pp. 1255-1265, 2024, doi: 10.1109/TCSVT.2023.3289147.（CCF-B, Top Journal）

• This work achieves a superior accuracy-efficiency trade-off in face forgery detection by effectively integrating local artifact and global texture clues with minimal parameters and computational cost.

TMM 2023

Exposing Deepfake Face Forgeries with Guided Residuals
Z. Guo, G. Yang*, J. Chen, and X. Sun. IEEE Transactions on Multimedia, vol. 25, pp. 8458-8470, 2023, doi: 10.1109/TMM.2023.3237169.（CCF-B, Top Journal）

• This work expands the applications of the guided filter, and overcomes the potential bias in the prediction-based residuals.

• KBS 2025 KAD-Net: Kolmogorov-Arnold and Differential-Aware Networks for Robust and Sensitive Proactive Deepfake Forensics

  S. He, Y. Diao, Y. Li, C. Sun, L. Wang*, and Z. Guo*. Knowledge-Based Systems, 2025. （Top Journal）[Code]

• INFFUS 2025 DiffMark: Diffusion-based Robust Watermark Against Deepfakes

  C. Sun, H. Sun, Z. Guo*, Y. Diao, L. Wang, D. Ma*, and G. Yang. Information Fusion, 2025. （Top Journal）[Code]

• ESWA 2023 Rethinking Gradient Operator for Exposing AI-enabled Face Forgeries

  Z. Guo, G. Yang*, D. Zhang, and M. Xia. Expert Systems with Applications, 2023. (Top Journal) [Code]

• CVIU 2023 A Data Augmentation Framework by Mining Structured Features for Fake Face Image Detection

  Z. Guo, G. Yang*, D. Wang, and D. Zhang. Computer Vision and Image Understanding, 2023. (CCF-B) [Code]

• CVIU 2021 Fake Face Detection via Adaptive Manipulation Traces Extraction Network

  Z. Guo, G. Yang*, J. Chen, and X. Sun. Computer Vision and Image Understanding, 2021. (CCF-B) [Code] [Dataset]

🔎 Manipulation Detection and Location

AAAI 2026

Beyond Fully Supervised Pixel Annotations: Scribble-Driven Weakly-Supervised Framework for Image Manipulation Localization
S. Li, G. Yu, Z. Guo*, Y. Diao, D. Ma, and G. Yang. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026. (CCF-A) [Code]

• This study introduces a novel weakly-supervised framework for image manipulation localization using only scribble annotations, which outperforms fully-supervised methods by employing self-supervised consistency, dynamic feature modulation, and entropy minimization.

AAAI 2026

From Passive Perception to Active Memory: A Weakly Supervised Image Manipulation Localization Framework Driven by Coarse-Grained Annotations
Z. Guo#, D. Xi#, S. Li*, and G. Yang. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026. (CCF-A) [Code]

• This paper achieves fine-grained image manipulation localization with minimal annotation cost by leveraging coarse box prompts and a subconscious memory-inspired feature fusion strategy.

🏥 Medical Image Processing

ICCV 2025

Similarity Memory Prior is All You Need for Medical Image Segmentation
H. Tang, Z. Guo*, L. Wang*, and C. Liu. IEEE/CVF International Conference on Computer Vision (ICCV), 2025. (CCF-A, Highlight Paper) [Code]

• This paper breaks through the limitations of traditional methods and constructs a category modeling based medical image segmentation network.

• INFFUS 2025 Conv-SdMLPMixer: A Hybrid Medical Image Classification Network Based on Multi-branch CNN and Multi-scale multi-dimensional MLP

  Z. Ren#, S Liu#, L. Wang*, and Z. Guo*. Information Fusion, 2025. （Top Journal） [Code]

• ASC 2024 Medical Image Segmentation Network Based on Feature Filtering with Low Number of Parameters

  Z. Ren#, Z. Guo#, L. Wang*, L. Xu, and C. Liu. Applied Soft Computing, 2024. （Top Journal）

📽️ Other Vision Tasks

• EAAI 2026 Multi-angle Feature Enhancement for Multi-defect Category Insulator Defect Detection in the Wild

  Z. Yang#, Z. Guo#, and L. Wang*. Engineering Applications of Artificial Intelligence, 2026. (Top Journal) [Code]

• PR 2025 CLTR: Continual Learning Time-varying Regularization for Robust Classification of Noisy Label Images

  Y. Li#, Z. Guo#, and L. Wang*. Pattern Recognition, 2025. (CCF-B, Top Journal) [Code]

• ESWA 2025 CTIFTrack: Continuous Temporal Information Fusion for Object Track

  Z. Zhang#, Z. Guo#, L. Wang*, and Y. Li. Expert Systems with Applications, 2025. （Top Journal） [Code]

• IPM 2024 TBC-MI : Suppressing noise labels by maximizing cleaning samples for robust image classification

  Y. Li, Z. Guo, L. Wang*, and L. Xu. Information Processing & Management, 2024. (CCF-B, Top Journal)

• ACM-TOMM 2023 Enhancing Adversarial Embedding based Image Steganography via Clustering Modification Directions

  D. Wang, G. Yang*, Z. Guo, and J. Chen. ACM Transactions on Multimedia Computing Communications and Applications, 2023. (CCF-B)

• CVIU 2022 Robust Detection of Dehazed Images via Dual-stream CNNs with Adaptive Feature Fusion

  J. Chen, G. Yang*, X. Ding, Z. Guo, and S. Wang. Computer Vision and Image Understanding, 2022. (CCF-B)

🤝 Academic Services

Editorial Board Member：

• International Journal of Autonomous and Adaptive Communications Systems

Technical Committee：

• Member, China Society of Image and Graphics (CSIG), Digital Forensics and Security Technical Committee.
• Member, China Computer Federation (CCF), Computer Vision Technical Committee.
• Member, China Computer Federation (CCF), Artificial Intelligence and Pattern Recognition Technical Committee.

Journal Reviewer:

• IEEE Transactions on Pattern Analysis and Machine Intelligence
• IEEE Transactions on Information Forensics and Security
• IEEE Transactions on Dependable and Secure Computing
• IEEE Transactions on Multimedia
• IEEE Transactions on Circuits and Systems for Video Technology
• IEEE Transactions on Cybernetics
• ACM Transactions on Multimedia Computing, Communications, and Applications
• Pattern Recognition
• Computer Vision and Image Understanding
• Information Fusion
• Knowledge-Based Systems

Conference Reviewer:

• AAAI Conference on Artificial Intelligence
• International Conference on Multimedia and Expo

💬 Contacts

• Address: No.777, Huarui Road, Urumqi, China.
• Email: guozhiqing [at] xju [dot] edu [dot] cn