Survey on industrial image anomaly detection based on unsupervised learning

Xu Jian; Feng Jiancong; Hou Fuhao; Yang Geng

doi:10.11959/j.issn.2096-109x.AQ25287

您当前的位置：

首页 >

文章列表页 >

Survey on industrial image anomaly detection based on unsupervised learning

Comprehensive Reviews | 更新时间：2026-07-15

- Survey on industrial image anomaly detection based on unsupervised learning
- Chinese Journal of Network and Information Security Vol. 12, Issue 3, Pages: 1-22(2026)
- 作者机构：
  
  1.南京邮电大学计算机学院，江苏南京 210023
  2.南京邮电大学大数据安全与智能处理江苏省高校重点实验室，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62372244)
- DOI：10.11959/j.issn.2096-109x.AQ25287
  CLC： TP393
- Received：28 December 2025，
  
  Revised：2026-04-15，
  
  Accepted：16 April 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
许建, 冯健聪, 侯富豪, 等. 基于无监督学习的工业图像异常检测研究综述[J]. 网络与信息安全学报, 2026, 12(3): 1-22.

Xu Jian, Feng Jiancong, Hou Fuhao, et al. Survey on industrial image anomaly detection based on unsupervised learning[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 1-22.
许建, 冯健聪, 侯富豪, 等. 基于无监督学习的工业图像异常检测研究综述[J]. 网络与信息安全学报, 2026, 12(3): 1-22. DOI： 10.11959/j.issn.2096-109x.AQ25287.

Xu Jian, Feng Jiancong, Hou Fuhao, et al. Survey on industrial image anomaly detection based on unsupervised learning[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 1-22. DOI： 10.11959/j.issn.2096-109x.AQ25287.

摘要

工业4.0推动了智能制造的快速发展，传统人工质检存在效率低、成本高和稳定性差等问题，而基于监督学习的工业图像异常检测方法高度依赖精确标注的异常样本，难以适应真实工业场景中缺陷稀少、多变的特点。相比之下，基于无监督学习的工业图像异常检测方法仅需利用正常样本建模正常产品的分布特征与内在模式，因而成为近年来学术界与工业界的研究热点。首先，围绕单类学习设定对基于无监督学习的工业图像异常检测方法进行系统综述，并依据异常分数来源将现有方法归纳为特征嵌入和图像重建两大范式，对相关研究的发展脉络与核心思想进行梳理；其次，在上述分类框架下，对两类方法的技术演进与研究趋势进行归纳，总结不同方法在特征表示、异常评分机制和定位策略等方面的共性与差异，并对现有研究成果的主要优势与局限进行综合分析，从整体上梳理当前基于无监督学习的工业图像异常检测方法的发展特点与研究重点；再次，归纳了该领域常用的基准数据集与评价指标；最后，从实际工业应用需求出发，对当前研究面临的主要问题进行概括，并对未来的发展方向进行展望，以期为该领域的后续研究与应用提供参考。

Abstract

With the rapid advancement of Industry 4.0

Intelligent manufacturing has become increasingly important. Traditional manual inspection still suffers from low efficiency

high cost

and limited stability

while supervised learning methods rely heavily on accurately labeled anomalous samples

which are scarce and diverse in real industrial scenarios. In contrast

unsupervised industrial image anomaly detection learns the distribution and intrinsic patterns of normal samples only

and has therefore attracted growing attention from both academia and industry.First

a systematic review of unsupervised industrial image anomaly detection under the one-class learning setting was provided. Existing methods were categorized into two main paradigms according to the source of anomaly scores: feature embedding and image reconstruction

and the development of representative approaches was summarized.Second

based on this taxonomy

the evolution and research trends of these two paradigms were analyzed. The common characteristics and differences among existing methods in terms of feature representation

anomaly scoring mechanisms

and localization strategies were summarized to provide a structured understanding of current research.Then

commonly used benchmark datasets and evaluation metrics in this field were reviewed.Finally

the main challenges in current research were discussed from the perspective of practical industrial applications

and potential future research directions were outlined to facilitate further development and deployment of unsupervised industrial image anomaly detection methods.

关键词

Keywords

references

Tao X , Gong X , Zhang X , et al . Deep learning for unsupervised anomaly localization in industrial images: a survey [J ] . IEEE Transactions on Instrumentation and Measurement , 2022 , 71 : 1 - 21 .

Cui Y , Liu Z , Lian S . A survey on unsupervised anomaly detection algorithms for industrial images [J ] . IEEE Access , 2023 , 11 : 55297 - 55315 .

Naeem S , Ali A , Anam S , et al . An unsupervised machine learning algorithms: comprehensive review [J ] . International Journal of Computing and Digital Systems , 2023 , 13 ( 1 ): 911 - 921 .

Gui J , Chen T , Zhang J , et al . A survey on self-supervised learning: algorithms, applications, and future trends [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024 , 46 ( 12 ): 9052 - 9071 .

Zhang J , Yang L , Mohammadabadi S M S , et al . A survey on self-supervised learning: recent advances and open problems [J ] . Neurocomputing , 2025 : 131409 .

Khan A , Sohail A , Fiaz M , et al . A survey of the self supervised learning mechanisms for vision transformers [PP ] . arXiv preprint , 2025 , arXiv: 2408.17059 .

Liu J , Xie G , Wang J , et al . Deep industrial image anomaly detection: a survey [J ] . Machine Intelligence Research , 2024 , 21 ( 1 ): 104 - 135 .

Salehi M , Sadjadi N , Baselizadeh S , et al . Multiresolution knowledge distillation for anomaly detection [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2021 : 14902 - 14912 .

Wang G , Han S , Ding E , et al . Student-teacher feature pyramid matching for anomaly detection [PP ] . arXiv preprint , 2021 , arXiv: 2103.04257 .

Cao Y , Wan Q , Shen W , et al . Informative knowledge distillation for image anomaly segmentation [J ] . Knowledge-Based Systems , 2022 , 248 : 108846 .

Rudolph M , Wehrbein T , Rosenhahn B , et al . Asymmetric student-teacher networks for industrial anomaly detection [C ] // Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . Piscataway : IEEE Press , 2023 : 2592 - 2602 .

Gu H , Li G , Liu Z . Masked feature regeneration based asymmetric student–teacher network for anomaly detection [J ] . Multimedia Tools and Applications , 2024 , 83 ( 42 ): 90573 - 90594 .

Zhang S , Liu J . Feature-constrained and attentionconditioned distillation learning for visual anomaly detection [C ] // Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE Press , 2024 : 2945 - 2949 .

Wei S , Jiang J , Xu X . UniNet: a contrastive learning-guided unified framework with feature selection for anomaly detection [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2025 : 9994 - 10003 .

黄诗瑀 , 叶锋 , 黄添强 , 等 . 人脸伪造与检测中的对抗攻防综述 [J ] . 网络与信息安全学报 , 2023 , 9 ( 4 ): 1 - 15 .

Huang S Y , Ye F , Huang T Q , et al . Survey on adversarial attacks and defense of face forgery and detection [J ] . Chinese Journal of Network and Information Security , 2023 , 9 ( 4 ): 1 - 15 .

Yi J H , Yoon S . Patch SVDD: patch-level SVDD for anomaly detection and segmentation [C ] // Proceedings of the 15th the Asian Conference on Computer Vision . Berlin : Springer , 2020 : 375 - 390 .

Tax D M J , Duin R P W . Support vector domain description [J ] . Pattern Recognition Letters , 1999 , 20 ( 11 ): 1191 - 1199 .

Liznerski P , Ruff L , Vandermeulen R A , et al . Explainable deep one-class classification [PP ] . arXiv preprint , 2021 , arXiv: 2007. 01760 .

Reiss T , Cohen N , Bergman L , et al . PANDA: adapting pretrained features for anomaly detection and segmentation [C ] // Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2021 : 2805 - 2813 .

Li C L , Sohn K , Yoon J , et al . CutPaste: self-supervised learning for anomaly detection and localization [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2021 : 9664 - 9674 .

Liu Z K , Zhou Y M , Xu Y S , et al . SimpleNet: a simple network for image anomaly detection and localization [C ] // Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2023 : 20402 - 20411 .

Rolih B , Fučka M , Skočaj D . SuperSimpleNet: unifying unsupervised and supervised learning for fast and reliable surface defect detection [C ] // Proceedings of the International Conference on Pattern Recognition . Cham : Springer Nature Switzerland , 2024 : 47 - 65 .

Chen Q Y , Luo H Y , Lv C K , et al . A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization [C ] // Proceedings of the 2024 European Conference on Computer Vision . Cham : Springer Nature Switzerland , 2024 : 37 - 54 .

Chen Q Y , Luo H Y , Gao H , et al . Progressive boundary guided anomaly synthesis for industrial anomaly detection [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 35 ( 2 ): 1193 - 1208 .

Dinh L , Sohl-Dickstein J , Bengio S . Density estimation using real nvp [PP ] . arXiv preprint , 2017 , arXiv: 1605.08803 .

Rudolph M , Wandt B , Rosenhahn B . Same same but differnet: Semi-supervised defect detection with normalizing flows [C ] // Proceedings of the 2021 IEEE/CVF Winter Conference on Applications of Computer Vision . Piscataway : IEEE Press , 2021 : 1906 - 1915 .

Rudolph M , Wehrbein T , Rosenhahn B , et al . Fully convolutional crossscale-flows for image-based defect detection [C ] // Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision . Piscataway : IEEE Press , 2022 : 1829 - 1838 .

Gudovskiy D , Ishizaka S , Kozuka K . CFLOW-AD: realtime unsupervised anomaly detection with localization via conditional normalizing flows [C ] // Proceedings of the 2022 IEEE/CVFWinter Conference on Applications of Computer Vision . Piscataway : IEEE Press , 2022 : 1819 - 1828 .

Milković F , Posilović L , Medak D , et al . FRAnomaly: flow-based rapid anomaly detection from images [J ] . Applied Intelligence , 2024 , 54 ( 4 ): 3502 - 3515 .

Zhou Y X , Xu X , Song J K , et al . MSFlow: multiscale flow-based framework for unsupervised anomaly detection [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2024 , 36 ( 2 ): 2437 - 2450 .

Lei J R , Hu X B , Wang Y , et al . PyramidFlow: high-resolution defect contrastive localization using pyramid normalizing flow [C ] // Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2023 : 14143 - 14152 .

Wu X Z , Mao G J , Xing S L . Unsupervised anomaly detection in images using attentional normalizing flows [J ] . Engineering Applications of Artificial Intelligence , 2024 , 127 : 107369 .

Park H , Noh J , Ham B . Learning memory-guided normality for anomaly detection [C ] // Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2020 : 14360 - 14369 .

Roth K , Pemula L , Zepeda J , et al . Towards total recall in industrial anomaly detection [C ] // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2022 : 14298 - 14308 .

Lee J C , Kim T , Park E , et al . Continuous memory representation for anomaly detection [C ] // Proceedings of the 2024 European Conference on Computer Vision . Cham : Springer Nature Switzerland , 2024 : 438 - 454 .

Tian L , Zhao H Y , Lu R Y , et al . FOCT: few-shot industrial anomaly detection with foreground-aware online conditional transport [C ] // Proceedings of the 32nd ACM International Conference on Multimedia . New York : ACM , 2024 : 6241 - 6249 .

Yang M H , Wu P , Feng H . MemSeg: a semi-supervised method for image surface defect detection using differences and commonalities [J ] . Engineering Applications of Artificial Intelligence , 2023 , 119 : 105835 .

Bae J , Lee J H , Kim S . PNI: industrial anomaly detection using position and neighborhood information [C ] // Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE Press , 2023 : 6350 - 6360 .

Hyun J , Kim S , Jeon G , et al . ReConPatch: contrastive patch representation learning for industrial anomaly detection [C ] // Proceedings of the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision . Piscataway : IEEE Press , 2024 : 2041 - 2050 .

Radford A , Kim J W , Hallacy C , et al . Learning transferable visual models from natural language supervision [C ] // Proceedings of the International Conference on Machine Learning . London : PMLR , 2021 : 8748 - 8763 .

Zhou Q H , Pang G S , Tian Y , et al . AnomalyCLIP: object-agnostic prompt learning for zero-shot anomaly detection [PP ] . arXiv preprint , 2026 , arXiv: 2310.18961 .

Li X F , Zhang Z Z , Tan X , et al . PromptAD: learning prompts with only normal samples for few-shot anomaly detection [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2024 : 16848 - 16858 .

Gu Z P , Zhu B K , Zhu G B , et al . FiLo: zero-shot anomaly detection by fine-grained description and high-quality localization [C ] // Proceedings of the 32nd ACM International Conference on Multimedia . New York : ACM , 2024 : 2041 - 2049 .

Yun E G , Ha H , Nam Y , et al . Language-assisted feature transformation for anomaly detection [PP ] . arXiv preprint , 2025 , arXiv: 2503. 01184 .

Gu Z P , Zhu B K , Zhu G B , et al . AnomalyGPT: detecting industrial anomalies using large vision-language models [J ] Proceedings of the AAAI conference on artificial intelligence , 2024 , 38 ( 3 ): 1932 - 1940 .

Li Y Y , Zhang S T , Li K , et al . One-to-normal: anomaly personalization for few-shot anomaly detection [C ] // Proceedings of the Advances in Neural Information Processing Systems 37 (NeurIPS 2024) . Vancouver : NeurIPS , 2024 : 78371 - 78393 .

Deng H Q , Li X Y . Anomaly detection via reverse distillation from one-class embedding [C ] // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2022 : 9727 - 9736 .

Tong G X , Li Q Q , Song Y . Enhanced multi-scale features mutual mapping fusion based on reverse knowledge distillation for industrial anomaly detection and localization [J ] . IEEE Transactions on Big Data , 2024 , 10 ( 4 ): 498 - 513 .

Wang G , Zou Y S , He S L , et al . Anomaly detection and localization via reverse distillation with latent anomaly suppression [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2025 , 35 ( 10 ): 9592 - 9607 .

Liu X Y , Wang J Y , Leng B , et al . Unlocking the potential of reverse distillation for anomaly detection [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Vancouver : AAAI Press , 2025 , 39 ( 6 ): 5640 - 5648 .

Tien T D , Nguyen A T , Tran N H , et al . Revisiting reverse distillation for anomaly detection [C ] // Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2023 : 24511 - 24520 .

Sakurada M , Yairi T . Anomaly detection using autoencoders with nonlinear dimensionality reduction [C ] // Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis . New York : ACM , 2014 : 4 - 11 .

Tao X , Zhang D P , Ma W Z , et al . Unsupervised anomaly detection for surface defects with dual-siamese network [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 11 ): 7707 - 7717 .

Huang C Q , Xu Q W , Wang Y F , et al . Self-supervised masking for unsupervised anomaly detection and localization [J ] . IEEE Transactions on Multimedia , 2022 , 25 : 4426 - 4438 .

Zavrtanik V , Kristan M , Skočaj D . DSR: a dual subspace reprojection network for surface anomaly detection [C ] // Proceedings of the European Conference on Computer Vision . Cham : Springer Nature Switzerland , 2022 : 539 - 554 .

Zavrtanik V , Kristan M , Skočaj D . DRAEM–a discriminatively trained reconstruction embedding for surface anomaly detection [C ] // Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision . Piscataway : IEEE Press , 2021 : 8310 - 8319 .

Zhang X M , Xu M , Zhou X Z . RealNet: a feature selection network with realistic synthetic anomaly for anomaly detection [C ] // Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2024 : 16699 - 16708 .

Chen X L , Xu H T , Deng C H , et al . Implicit foreground-guided network for anomaly detection and localization [C ] // Proceedings of the ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE Press , 2024 : 2970 - 2974 .

Zhang R F , Wang H , Feng M Y , et al . Dual-constraint autoencoder and adaptive weighted similarity spatial attention for unsupervised anomaly detection [J ] . IEEE Transactions on Industrial Informatics , 2024 , 20 ( 7 ): 9393 - 9403 .

Zhou Z Y , Wang J C , Yu Z A , et al . FeatDAE: introducing features with denoising autoencoder for anomaly detection [J ] . IEEE Transactions on Instrumentation and Measurement , 2025 , 74 : 1 - 14 .

Luo W , Cao Y K , Yao H M , et al . Exploring intrinsic normal prototypes within a single image for universal anomaly detection [C ] // Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2025 : 9974 - 9983 .

Schlegl T , Seeböck P , Waldstein S M , et al . F-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks [J ] . Medical Image Analysis , 2019 , 54 : 30 - 44 .

Nakanishi H , Suzuki M , Matsuo Y . Iterative image inpainting with structural similarity mask for anomaly detection [C ] // the 9th International Conference on Learning Representations . 2021 .

Yan X D , Zhang H D , Xu X M , et al . Learning semantic context from normal samples for unsupervised anomaly detection [C ] // Proceedings of the AAAI conference on artificial intelligence . Vancouver : AAAI Press , 2021 , 35 ( 4 ): 3110 - 3118 .

Fang Q Q , Su Q L , Lyu W X , et al . Boosting fine-grained visual anomaly detection with coarse-knowledge-aware adversarial learning [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Vancouver : AAAI Press , 2025 : 16532 - 16540 .

Liang Y F , Zhang J N , Zhao S W , et al . Omni-frequency channel-selection representations for unsupervised anomaly detection [J ] . IEEE Transactions on Image Processing , 2023 , 32 : 4327 - 4340 .

Creswell A , Bharath A A . Inverting the generator of a generative adversarial network [J ] . IEEE transactions on neural networks and learning systems , 2019 , 30 ( 7 ): 1967 - 1974 .

Mou S C , Gu X Y , Cao M , et al . RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection [PP ] . arXiv preprint , 2025 , arXiv: 2302. 12464 .

Vaswani A , Shazeer N , Parmar N , et al . Attention is all you need [C ] // Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems . New York : ACM , 2017 : 5998 - 6008 .

Pirnay J , Chai K . Inpainting transformer for anomaly detection [C ] // Proceedings of the International Conference on Image Analysis and Processing . Cham : Springer , 2022 : 394 - 406 .

Mishra P , Verk R , Fornasier D , et al . VT-ADL: a vision transformer network for image anomaly detection and localization [C ] // Proceedings of the 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) . New York : IEEE , 2021 : 1 - 6 .

Iqbal E , Khan S U , Javed S , et al . Multi-scale feature reconstruction network for industrial anomaly detection [J ] . Knowledge-Based Systems , 2024 , 305 : 112650 .

Tang J L , Lv Z , Liu Y , et al . An improved transformer U-Net-based reconstruction method for unsupervised anomaly detection [C ] // Proceedings of the 2024 14th Asian Control Conference (ASCC) . Piscataway : IEEE Press , 2024 : 153 - 158 .

Luo W , Yao H M , Yu W Y . Template-based feature aggregation network for industrial anomaly detection [J ] . Engineering Applications of Artificial Intelligence , 2024 , 131 : 107810 .

Yao H M , Cao Y K , Luo W , et al . Prior normality prompt transformer for multiclass industrial image anomaly detection [J ] . IEEE Transactions on Industrial Informatics , 2024 , 20 ( 10 ): 11866 - 11876 .

Ho J , Jain A , Abbeel P . Denoising diffusion probabilistic models [J ] . Advances in Neural Information Processing Systems , 2020 , 33 : 6840 - 6851 .

Wyatt J , Leach A , Schmon S M , et al . AnoDDPM: anomaly detection with denoising diffusion probabilistic models using simplex noise [C ] // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Piscataway : IEEE Press , 2022 : 649 - 655 .

Zhang H , Wang Z , Zeng D , et al . DiffusionAD: norm-guided one-step denoising diffusion for anomaly detection [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025 , 47 ( 8 ): 7140 - 7152 .

Yao H , Liu M , Yin Z C , et al . GLAD: towards better reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection [C ] // Proceedings of 2024 the European Conference on Computer Vision . Cham : Springer Nature Switzerland , 2024 : 1 - 17 .

Mousakhan A , Brox T , Tayyub J . Anomaly detection with conditioned denoising diffusion models [C ] // Proceedings of the 46th DAGM German Conference on Pattern Recognition . Cham : Springer Nature Switzerland , 2024 : 181 - 195 .

He H Y , Zhang J N , Chen H X , et al . A diffusion-based framework for multi-class anomaly detection [C ] // Proceedings of the AAAI conference on artificial intelligence . Vancouver : AAAI Press , 2024 , 38 : 8472 - 8480 .

Beizaee F , Lodygensky G A , Desrosiers C , et al . Correcting deviations from normality: a reformulated diffusion model for multiclass unsupervised anomaly detection [C ] // Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2025 : 19088 - 19097 .

Fučka M , Zavrtanik V , Skočaj D . TransFusion: a transparency-based diffusion model for anomaly detection [C ] // Proceedings of the 18th European Conference on Computer Vision . New York : ACM , 2024 : 91 - 108 .

侯壹凡 , 苗壮 , 王俊 , 等 . 融合色彩空间特征与频域特征的多源伪造图像检测方法 [J ] . 网络与信息安全学报 , 2026 , 12 ( 1 ): 141 - 150 .

Hou Y F , Miao Z , Wang J , et al . Multi-source forgery detection method based on the fusion of color space features and frequency domain features [J ] . Chinese Journal of Network and Information Security , 2026 , 12 ( 1 ): 141 - 150 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Security, privacy, trust and resilience in the service-based mobile networks: a comprehensive survey

Accelerating homomorphic ReLU via segmented lookup table and dynamic CG-NTT reuse

Cyber threat attribution framework integrating threat intelligence knowledge graph and graph constrained reasoning

Intelligent mining of App’s potential data sharing behavior

Spatiotemporally parallel reconfigurable computing array architecture for multi-data stream processing

Related Author

No data

Related Institution

No data

⁰