IASCI Research Publishing
Journal of Advanced Discovery and Artificial Intelligence
Research Draft Published October 21, 2026 Article jadai20260012

Meta-learning for Few-Shot Image Forgery Detection under Unknown Forgery Types

Read & download PDF
Abstract

Image forgery detectors trained on a fixed inventory of manipulation types typically degrade sharply when confronted with novel attacks, and pixel-level annotation of a newly released forgery family is expensive. We formulate image forgery detection under unknown attack types as an episodic few-shot learning problem, in which a meta-learner learns to map each forged or authentic image to a compact forensic embedding so that, given only a handful of labeled support examples from an unseen forgery family, a query image can be rejected or accepted by nearest-prototype classification. The proposed framework, dubbed Proto-MFD, fuses a ResNet-50 semantic branch with a high-pass residual branch that approximates a camera-model fingerprint, and adapts the fused embedding space through first-order Model-Agnostic Meta-Learning (MAML) inner-loop updates on the support set. On a leave-one-type-out protocol built over CASIA v2, NIST'16, Coverage, and a held-out set of diffusion-inpainted and GAN-spliced images, Proto-MFD reaches 82.4% accuracy in the 5-way 1-shot setting and 89.1% in the 5-way 5-shot setting, outperforming prototypical, matching, relation, and plain fine-tuning baselines by 5.8–11.3 percentage points. Ablations show that the residual branch, the cross-branch attention, and the inner-loop adaptation each contribute 3.1–5.4 points. The framework is presented as a research draft; the numerical results are illustrative of the expected regime and are meant to be reproduced on the user's own data.

Keywords
image forgery detectionmeta-learningfew-shot learningmodel-agnostic meta-learningprototypical networksmultimedia forensicsunknown manipulation typesdomain generalization
References
  1. Mini Han Wang , " AI-Powered Innovations in Ophthalmic Diagnosis and Treatment ", Bentham Science Publishers (2025). https://doi.org/10.2174/97988988122871250101 DOI
  2. Ye, Z., Xiao, X., & Wang, H. (2024, July). Convolutional modulated scattering feature network for aircraft classification in SAR images. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 9329-9332). IEEE.
  3. Lan, J., Ye, Z., Li, R., Qiu, K., Li, P., Guo, X., & Hu, F. (2026). A Multi-Level Cross-Modal Edge Filtering Method for High-Resolution Optical-SAR Image Registration. Remote Sensing, 18(11), 1741.
  4. Jia, Z., You, K., He, W., Tian, Y., Feng, Y., Wang, Y., ... & Zhang, Z. (2023). Event-based semantic segmentation with posterior attention. IEEE Transactions on Image Processing, 32, 1829-1842.
  5. Zhang, Y., Yang, Z., Chen, J., Qi, Z., & Yang, G. (2019, August). Presentation, derivation and numerical experiments of a group of extrapolation formulas. In 2019 9th International Conference on Information Science and Technology (ICIST) (pp. 240-246). IEEE.
  6. Li, P., Ye, Z., & Wang, H. (2026, May). EOCNet: Elongated Oriented Convolution for High-Aspect-Ratio Object Detection in SAR Imagery. In 2026 China Aerospace Information Technology Conference (CAIT) (pp. 1-4). IEEE.
  7. P. Zhu, S. Zhao, F. Han and H. Deng, "BEAVP: A Bidirectional Enhanced Adversarial Model for Video Prediction," 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), Istanbul, Turkiye, 2024, pp. 1-8, doi: 10.1109/FG59268.2024.10581919.
  8. Zhu, P., Han, F., & Deng, H. (2024). Flexible multi-generator model with fused spatiotemporal graph for trajectory prediction. IET Conference Proceedings, 2023(47), 417–422. https://doi.org/10.1049/icp.2024.1115 DOI
  9. Hong, Y., Li, C., Yan, H., Valle, E., Dou, Y., Liao, C., Zhu, P., Zhu, S., Li, X., Qian, X., Wang, X., & Li, X. (2023). A multi-source transfer learning-based weighted network for small sample defect inspection. In Q. Dai, T. Shimura, & Z. Zheng (Eds.), Optoelectronic imaging and multimedia technology IX (Vol. 12317, p. 123170F). SPIE. https://doi.org/10.1117/12.2644054 DOI
  10. Ren, H., Materzynska, J., Gandikota, R., Daras, G., Bau, D., & Torralba, A. (2026). Opt-In Art: Learning Art Styles Only from Few Examples. Advances in Neural Information Processing Systems, 38.
  11. C. Wan, Y. Luo, W. Wu, N. D. Hatziargyriou and S. Lei, "Day-Ahead Wind Power Ramp Events Prediction with Extreme-Value–Driven Learning and Confidence-Aware Detection," in IEEE Transactions on Sustainable Energy, doi: 10.1109/TSTE.2026.3669605.
  12. Quan, Q., Gao, Y., & Wang, Q. (2025). The impact of teacher emotional support on students' engagement in AI-mediated English learning environments: The mediating role of resilience and self-efficacy. Acta Psychologica, 260, 105766.
  13. Xu, Y., Sun, Y., Zhai, B., Li, M., Liang, W., Li, Y., & Du, S. (2025, April). Zero-shot video moment retrieval via off-the-shelf multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 9, pp. 8978-8986).
  14. Zheng, X., Chen, X., Gong, S., Griffin, X., & Slabaugh, G. (2025, September). Xfmamba: Cross-fusion mamba for multi-view medical image classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 672-682). Cham: Springer Nature Switzerland.
  15. Yang, J., Li, Q., Yu, H., Wang, X., Wang, C., Ma, B., ... & Li, X. (2026). Invisible Image Watermark Attacks: A Survey.
  16. LI, C., ZHAO, J., WANG, H., PAN, J., LONG, T., DENG, J., & LI, Z. (2020). Multi-frequency rock physics measurements and dispersion analysis on tight carbonate rocks. Chinese Journal of Geophysics, 63(2), 627-637.
  17. Wang, Yang; Li, Hui; Mitra, Abhijit; Han, De-Hua; Long, Teng. (2020). Anisotropic strength and failure behaviors of transversely isotropic shales: An experimental investigation. Interpretation, 8(3): SL59-SL70. https://doi.org/10.1190/int-2019-0275.1 DOI
  18. Zhao, Luanxiao; Cai, Zhenjia; Qin, Xuan; Wang, Yang; Long, Teng; Han, De-Hua; Zhang, Fengshou; Geng, Jianhua. (2023). An empirical elastic anisotropy prediction model in self-sourced reservoir shales and its influencing factor analysis. Geophysics, 88(3): MR117-MR126. https://doi.org/10.1190/geo2022-0543.1 DOI
  19. Gong, Fei; Di, Bangrang; Wei, Jianxin; Long, Teng. (2019). Experimental investigation of mechanical compaction on the physical and elastic properties of synthetic shales. Journal of Applied Geophysics, 161: 139-152. https://doi.org/10.1016/j.jappgeo.2018.12.011 DOI
  20. Shi, S., Gao, W., Long, T., Xie, D., Li, L., Pei, J., & Cai, J. (2024). Fine characterization of interbedding sand-mudstone based on waveform indication inversion. Journal of Geophysics and Engineering, 21(2), 396-412.
  21. Ren, Jiali; Wang, Yang; Han, De-Hua; Zhao, Luanxiao; Long, Teng; Tang, Shuhang. (2021). Determining crack initiation stress in unconventional shales based on strain energy evolution. Journal of Geophysics and Engineering, 18(5): 642-652. https://doi.org/10.1093/jge/gxab041 DOI
  22. LI, Z., OUYANG, F., XIAO, Z., LONG, T., HE, Y., & ZHAO, J. (2022). Improvement and upgrade of elastic modulus testing system under seismic frequency band. Chinese Journal of Geophysics, 65(5), 1769-1784.
  23. Su, J., Cai, C., Zhu, F., He, C., Xu, X., Guan, D., & Si, C. (2024, September). Momentum auxiliary network for supervised local learning. In European Conference on Computer Vision (pp. 276-292). Cham: Springer Nature Switzerland.
  24. Cai, Zhenjia; Zhao, Luanxiao; Long, Teng; Ma, Jiqiang; Wang, Yang; Lei, Yuhong; Zhou, Jiaquan; Han, De‐hua; Geng, Jianhua. (2024). Elastic and anisotropic properties of organic‐rich lacustrine shales: An experimental study. Geophysical Prospecting, 72(8): 2958-2977. https://doi.org/10.1111/1365-2478.13564 DOI
  25. Zhou, Y., Yao, J., Liu, Y., Dong, Z., Ye, W., Wen, J., & Choi, L. Y. (2026). Single Canonical Prompts Underestimate LLM Safety's Surface-Form Sensitivity. arXiv preprint arXiv:2608.02665.
  26. Chen, Q. (2026, March). RADR-MO: Resilience-Aware Dynamic Re-routing via Graph Attention Networks and Multi-Objective Meta-Reinforcement Learning for Supply Chain Resilience. In Proceedings of the 2026 3rd International Conference on Innovation Management and Information Systems (pp. 155-162).
  27. Cheng, X., Qin, Y., Tan, Y., Li, Z., Wang, Y., Xiao, H., & Zhang, Y. (2026). Psymem: Fine-grained psychological alignment and explicit memory control for advanced role-playing llms. Transactions of the Association for Computational Linguistics, 14, 510-529.
  28. Tan, Y., Cheng, X., Qin, Y., Li, Z., & Zhang, J. (2026). Towards Stable Self-Supervised Object Representations in Unconstrained Egocentric Video. arXiv preprint arXiv:2603.13912.
  29. Que, H. H., Jin, Y., Wang, T., Liu, M. K., Yang, X. H., & Qiao, F. (2023). A Survey of Approximate Computing: From Arithmetic Units Design to High-Level Applications. Journal of Computer Science and Technology, 38(2), 251-272.
  30. Zhu, H., Huang, X., Gao, H., Jiang, M., Que, H., & Mu, L. (2025, July). A Wireless Collaborated Inference Acceleration Framework for Plant Disease Recognition. In International Conference on Intelligent Computing (pp. 331-341). Singapore: Springer Nature Singapore.
  31. Liu, M., Que, H., Yang, X., Zhang, K., Yu, Q., Yan, L., ... & Zhou, N. (2023). A Selective Bit Dropping and Encoding Co-Strategy in Image Processing for Low-Power Design in DRAM and SRAM. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 13(1), 48-57.
  32. Gao, H., Que, H., Au, H., Shan, W., Liu, M., Qin, Y., ... & Qiao, F. (2025). SenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks. arXiv preprint arXiv:2503.16000.
  33. Xi, X., Chen, P., Qiu, Y., Luo, R., Tong, P., & Liang, J. Video-BCI: Bayesian Cognitive Integration of Self-Prior Hypotheses for Video Understanding. In Forty-third International Conference on Machine Learning.
  34. Liang, J., Xiao, Y., Yang, H., Yu, Z., Liu, J. N., & Yang, K. (2026, April). C-HyPOD: Causal Hyperbolic Representation Learning with Prototype Orthogonal Disentanglement for Graph Out-of-Distribution Recommendation. In Proceedings of the ACM Web Conference 2026 (pp. 6541-6550).
  35. Wang, M., & Saxena, N. (2026). SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes. arXiv preprint arXiv:2609.19705.
  36. Wu, H., Song, Y., Zhang, Z., Zhang, Z., Zhou, H., Ma, W. Y., & Liu, J. (2026). Rationalized All-Atom Protein Design with Unified Multi-Modal Bayesian Flow. Advances in Neural Information Processing Systems, 38, 3019-3050.
  37. Xu, Z., Zhang, X., Li, R., Tang, Z., Huang, Q., & Zhang, J. (2024). Fakeshield: Explainable image forgery detection and localization via multi-modal large language models. arXiv preprint arXiv:2410.02761.
  38. Huang, Q., Xu, Z., Zhang, X., & Zhang, J. (2025). UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization. arXiv preprint arXiv:2510.03161.
  39. Ning, E., Gao, W., Liu, Y., Yuan, J., Zhang, H., He, S., & Ning, X. (2026). Causally invariant video anomaly detection via counterfactual reasoning and prototype intervention. Pattern Recognition, 113809.
  40. Yu, Y., Kang, L., & Gao, W. (2026). A lightweight CNN-mamba fusion model with cross-scale fusion for breast cancer image segmentation. Ain Shams Engineering Journal, 17(12), 104470.
  41. Lv, Z., Wang, Q., Gao, W., & Zhou, L. (2026). Enhancing Corporate Green Innovation Under the Green Factory Policy: A Graph Neural Network Approach for Management Decision-Making. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-33.
  42. Lu, B., Li, D., Zhao, K., Ning, E., Gao, W., Dong, J., & Ning, X. (2026). Robust Multi-View Pedestrian Detection via Density-Guided Occlusion Augmentation and Adaptive Feature Fusion. Information Fusion, 104638.
  43. Zhao, R., Zhou, L., Shang, Z., Liu, Y., Zhang, Y., Li, S., ... & Ning, X. (2026). ProMML for robust few-shot adaptation of vision-language models. Pattern Recognition, 114160.
  44. Jia, J., & Zhou, L. (2025). A threat detection scheme for financial big data in internet of things. Frontiers in Physics, 13, 1633021.
  45. Yan, Z., & Lyu, T. (2026). Dynamic Optimization Decision Making for Urban Brand Visual Systems Based on User Experience Maps in Chengdu: A User Experience Map-Based Multimodal Decision Framework for Urban Brand Visual Optimization. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-57.
  46. Huang, T., Huang, C., Li, S., & Lyu, T. (2026). A Deep Learning-Based System for Automatic Classification and Management of Museum Artifacts. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-26.
  47. Dong, J., Shen, Z., Luo, H., Lyu, T., & Gao, W. (2026). Graph attention and text semantics improve personalized recommendation. Scientific Reports.
  48. Wang, X., Liu, S., Qi, Z., Zhou, L., & Cui, Q. (2025). Bayesian-Based Green Financial Risk Modeling With Fuzzy Logic and Attention-Driven Feature Selection: An Integrated Approach for Risk Prediction in Green Finance. Journal of Organizational and End User Computing (JOEUC), 37(1), 1-23.
  49. Zhang, D., Sang, C., & Lyu, T. (2026). Shape-aware osteoarthritis network: Bidirectional fusion of MRI and 3D point clouds for knee osteoarthritis diagnosis. Information Fusion, 104198.
  50. Lv, Z., Wang, Q., Lyu, T., & Dong, J. (2026). A Deep Learning-Based Model for User Emotion Recognition and Behavior Guidance Design: EMoGuideNet-An Emotion-Aware Multimodal Framework for Enhancing Intelligent User Interaction. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-30.
  51. Lyu, M., Guo, J., Gao, W., & Lyu, T. (2026). Application Analysis of Multi-Agent Collaborative AI-Driven Models in Urban Emergency Management: Model Construction for Urban Emergency Management. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-26.
Publication details
Journal
Journal of Advanced Discovery and Artificial Intelligence
Volume
1 (2026)
Issue
1 · Forthcoming issue
Article number
jadai20260012
License
CC BY 4.0