[1]张汝波,蔺庆龙,张天一.基于深度学习的图像篡改检测方法综述[J].智能系统学报,2025,20(2):283-304.[doi:10.11992/tis.202403004]
ZHANG Rubo,LIN Qinglong,ZHANG Tianyi.A review of image tampering detection methods based on deep learning[J].CAAI Transactions on Intelligent Systems,2025,20(2):283-304.[doi:10.11992/tis.202403004]
点击复制
ZHANG Rubo,LIN Qinglong,ZHANG Tianyi.A review of image tampering detection methods based on deep learning[J].CAAI Transactions on Intelligent Systems,2025,20(2):283-304.[doi:10.11992/tis.202403004]
基于深度学习的图像篡改检测方法综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第2期
页码:
283-304
栏目:
综述
出版日期:
2025-03-05
- Title:
- A review of image tampering detection methods based on deep learning
- Keywords:
- deep learning; image tempering detection; computer vision; convolutional neural network; image processing; image forensic; image forgery; forgery detection
- 分类号:
- TP39
- 摘要:
- 随着数字图像编辑工具的普及，图像篡改变得越来越容易，大量被篡改后的虚假图像通过网络和社交媒体进行传播，这对法律、新闻媒体和科学研究等领域的真实性和可信度构成了威胁。图像篡改检测的目的是检测和定位篡改图像中的篡改区域，以保护图像的可信度。本文对基于深度学习的篡改检测方法进行了回顾总结。首先，介绍了目前图像篡改检测领域的研究现状。其次，对近5年的深度学习方法进行了分类整理。然后，介绍了主要的数据集和评价指标，以及各种方法的性能对比。最后，探讨了目前篡改检测方法的局限性并对未来的发展方向进行了展望。
- Abstract:
- With the increasing popularity of digital image editing tools, image tampering has become much easier. A large number of tampered false images are now circulating on the Internet and social media, threatening the authenticity and credibility of critical domains such as law, journalism, and scientific research. Image tampering detection aims to identify and locate altered areas within tampered images, thereby safeguarding their credibility. This paper provides a comprehensive review of deep learning-based methods for image tampering detection. First, it introduces the current research status in this field. Next, it classifies deep learning approaches developed over the past five years. The paper also highlights the main datasets and evaluation metrics used, along with a performance comparison of various methods. Finally, it discusses the limitations of current tampering detection methods and offers insights into future development directions.
- 参考文献/References:
-
[1] 田秀霞, 李华强, 张琴, 等. 基于双通道R-FCN的图像篡改检测模型[J]. 计算机学报, 2021, 44(2): 370-383.
TIAN Xiuxia, LI Huaqiang, ZHANG Qin, et al. Dual-channel R-FCN model for image forgery detection[J]. Chinese journal of computers, 2021, 44(2): 370-383.
[2] 胡林辉, 陈保营, 谭舜泉, 等. 基于Convnext-Upernet的图像篡改检测定位模型[J]. 计算机学报, 2023, 46(10): 2225-2239.
HU Linhui, CHEN Baoying, TAN Shunquan, et al. Convnext-upernet based deep-learning model for image forgery detection and localization[J]. Chinese journal of computers, 2023, 46(10): 2225-2239.
[3] 蔺琛皓, 沈超, 邓静怡, 等. 虚假数字人脸内容生成与检测技术[J]. 计算机学报, 2023, 46(3): 469-498.
LIN Chenhao, SHEN Chao, DENG Jingyi, et al. False digital facial content generation and detection technology[J]. Journal of computer science, 2023, 46(3): 469-498.
[4] 李昊东, 庄培裕, 李斌. 基于深度学习的数字图像篡改定位方法综述[J]. 信号处理, 2021, 37(12): 2278-2301.
LI Haodong, ZHUANG Peiyu, LI Bin. A survey on deep learning based digital image tampering localization methods[J]. Journal of signal processing, 2021, 37(12): 2278-2301.
[5] 孙鹏, 郎宇博, 樊舒, 等. 图像拼接篡改的自动色温距离分类检验方法[J]. 自动化学报, 2018, 44(7): 1321-1332.
SUN Peng, LANG Yubo, FAN Shu, et al. Detection of image splicing manipulation by automated classification of color temperature distance[J]. Acta automatica sinica, 2018, 44(7): 1321-1332.
[6] 李晓龙, 俞能海, 张新鹏, 等. 数字媒体取证技术综述[J]. 中国图象图形学报, 2021, 26(6): 1216-1226.
LI Xiaolong, YU Nenghai, ZHANG Xinpeng, et al. Overview of digital media forensics technology[J]. Journal of image and graphics, 2021, 26(6): 1216-1226.
[7] POPESCU A C, FARID H. Exposing digital forgeries in color filter array interpolated images[J]. IEEE transactions on signal processing, 2005, 53(10): 3948-3959.
[8] FARID H, LYU Siwei. Higher-order wavelet statistics and their application to digital forensics[C]//2003 Conference on Computer Vision and Pattern Recognition Workshop. Madison: IEEE, 2003: 94.
[9] FRIDRICH J, SOUKAL D, LUKAS J. Detection of copy-move forgery in digital images[C]//Proceedings of digital forensic research workshop. Cleveland: Elsevier, 2003: 652-662.
[10] 刘丽颖, 王金鑫, 曹少丽, 等. 检测小篡改区域的U型网络[J]. 中国图象图形学报, 2022, 27(1): 176-187.
LIU Liying, WANG Jinxin, CAO Shaoli, et al. U-Net for detecting small forgery region[J]. Chinese journal of image and graphics, 2022, 27(1): 176-187.
[11] 陈海鹏, 张世博, 吕颖达. 多尺度感知与边界引导的图像篡改检测方法[J/OL]. 吉林大学学报(工学版). [2024-03-04].https://doi.org/10.13229/j.cnki.jdxbgxb.20231027 .
CHEN Haipeng, ZHANG Shibo, LYU Yingda. Multi-scale context-aware and boundary-guided image manipulation detection method[J/OL]. Journal of Jilin University (engineering edition). [2024-03-04].https://doi.org/10.13229/j.cnki.jdxbgxb.20231027 .
[12] 魏伟一, 赵毅凡, 陈帼. 基于深度特征提取和DCT变换的图像复制粘贴篡改检测[J]. 计算机工程与科学, 2023, 45(1): 163-170.
WEI Weiyi, ZHAO Yifan, CHEN Guo. Image copy-move forgery detection based on deep feature extraction and DCT transform[J]. Computer engineering and science, 2023, 45(1): 163-170.
[13] 朱新同, 唐云祁, 耿鹏志. 数字图像篡改检测技术综述[J]. 中国人民公安大学学报(自然科学版), 2022, 28(4): 87-99.
ZHU Xintong, TANG Yunqi, GENG Pengzhi. Survey on digital image tampering detection technology[J]. Journal of People’s Public Security University of China (natural science edition), 2022, 28(4): 87-99.
[14] 左鑫兰. 图像篡改检测技术研究综述[J]. 长江信息通信, 2022, 35(3): 74-76.
ZUO Xinlan. Research of image manipulation detection technology[J]. Changjiang information and communication, 2022, 35(3): 74-76.
[15] BI Xiuli, WEI Yang, XIAO Bin, et al. RRU-Net: the ringed residual U-Net for image splicing forgery detection[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach: IEEE, 2019: 30-39.
[16] VERDOLIVA L. Media forensics and DeepFakes: an overview[J]. IEEE journal of selected topics in signal processing, 2020, 14(5): 910-932.
[17] MEHRJARDI F Z, LATIF A M, ZARCHI M S, et al. A survey on deep learning-based image forgery detection[J]. Pattern recognition, 2023, 144: 109778.
[18] PHAM N T, PARK C S. Toward deep-learning-based methods in image forgery detection: a survey[J]. IEEE access, 2023, 11: 11224-11237.
[19] ZANARDELLI M, GUERRINI F, LEONARDI R, et al. Image forgery detection: a survey of recent deep-learning approaches[J]. Multimedia tools and applications, 2023, 82(12): 17521-17566.
[20] 杨衍宇, 魏为民, 张运琴. 一种改进的双流Faster R-CNN图像篡改识别模型[J]. 计算机应用与软件, 2023, 40(12): 189-194.
YANG Yanyu, WEI Weimin, ZHANG Yunqin. Image forgery recognition model based on improved dual-stream faster R-CNN[J]. Computer applications and software, 2023, 40(12): 189-194.
[21] 付顺旺, 陈茜, 李智, 等. 用于篡改图像检测和定位的双通道渐进式特征过滤网络[J]. 计算机应用, 2024, 44(4): 1303-1309.
FU Shunwang, CHEN Qian, LI Zhi, et al. Two-channel progressive feature filtering network for tampered image detection and localization[J]. Journal of computer applications, 2024, 44(4): 1303-1309.
[22] ZHOU Peng, HAN Xintong, MORARIU V I, et al. Learning rich features for image manipulation detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1053-1061.
[23] ZHOU Peng, HAN Xintong, MORARIU V I, et al. Two-stream neural networks for tampered face detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1831-1839.
[24] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(6): 1137-1149.
[25] RAO Yuan, NI Jiangqun, ZHANG Weizhe, et al. Towards JPEG-resistant image forgery detection and localization via self-supervised domain adaptation[J]. IEEE transactions on pattern analysis and machine intelligence, 2022: 1-12.
[26] CHEN Xinru, DONG Chengbo, JI Jiaqi, et al. Image manipulation detection by multi-view multi-scale supervision[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 14165-14173.
[27] LI Dong, ZHU Jiaying, WANG Menglu, et al. Edge-aware regional message passing controller for image forgery localization[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 8222-8232.
[28] LIU Yaqi, LYU Binbin, JIN Xin, et al. TBFormer: two-branch Transformer for image forgery localization[J]. IEEE signal processing letters, 2023, 30: 623-627.
[29] BAYAR B, STAMM M C. Constrained convolutional neural networks: a new approach towards general purpose image manipulation detection[J]. IEEE transactions on information forensics and security, 2018, 13(11): 2691-2706.
[30] FU Jun, LIU Jing, TIAN Haijie, et al. Dual attention network for scene segmentation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 3146-3154.
[31] WU Yue, ABDALMAGEED W, NATARAJAN P. ManTra-net: manipulation tracing network for detection and localization of image forgeries with anomalous features[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 9543-9552.
[32] LIN Xun, WANG Shuai, DENG Jiahao, et al. Image manipulation detection by multiple tampering traces and edge artifact enhancement[J]. Pattern recognition, 2023, 133: 109026.
[33] DAS S, ISLAM M S, AMIN M R. GCA-net: utilizing gated context attention for improving image forgery localization and detection[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. New Orleans: IEEE, 2022: 81-90.
[34] WARIF N B A, IDRIS M Y I, WAHAB A W A, et al. An evaluation of error level analysis in image forensics[C]//2015 5th IEEE International Conference on System Engineering and Technology. Shah Alam: IEEE, 2015: 23-28.
[35] GUILLARO F, COZZOLINO D, SUD A, et al. TruFor: leveraging all-round clues for trustworthy image forgery detection and localization[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 20606-20615.
[36] COZZOLINO D, VERDOLIVA L. Noiseprint: a CNN-based camera model fingerprint[J]. IEEE transactions on information forensics and security, 2019, 15: 144-159.
[37] WANG Junke, WU Zuxuan, CHEN Jingjing, et al. ObjectFormer for image manipulation detection and localization[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 2354-2363.
[38] GUO Xiao, LIU Xiaohong, REN Zhiyuan, et al. Hierarchical fine-grained image forgery detection and localization[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 3155-3165.
[39] KWON M J, NAM S H, YU I J, et al. Learning JPEG compression artifacts for image manipulation detection and localization[J]. International journal of computer vision, 2022, 130(8): 1875-1895.
[40] ZHANG Zhenfei, LI Mingyang, CHANG M C. A new benchmark and model for challenging image manipulation detection[J]. Proceedings of the AAAI conference on artificial intelligence, 2024, 38(7): 7405-7413.
[41] GU A R, NAM J H, LEE S C. FBI-net: frequency-based image forgery localization via multitask learning with self-attention[J]. IEEE access, 2022, 10: 62751-62762.
[42] LIU Yaqi, ZHU Xiaobin, ZHAO Xianfeng, et al. Adversarial learning for constrained image splicing detection and localization based on atrous convolution[J]. IEEE transactions on information forensics and security, 2019, 14(10): 2551-2566.
[43] NILOY F F, KUMAR BHAUMIK K, WOO S S. CFL-net: image forgery localization using contrastive learning[C]//2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2023: 4631-4640.
[44] ZHONG Junliu, PUN C M. An end-to-end dense-InceptionNet for image copy-move forgery detection[J]. IEEE transactions on information forensics and security, 2019, 15: 2134-2146.
[45] HAO Jing, ZHANG Zhixin, YANG Shicai, et al. TransForensics: image forgery localization with dense self-attention[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 15035-15044.
[46] LIU Ze, LIN Yutong, CAO Yue, et al. Swin Transformer: hierarchical vision Transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 9992-10002.
[47] TOUVRON H, CORD M, SABLAYROLLES A, et al. Going deeper with image Transformers[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 32-42.
[48] ZHOU Zongwei, RAHMAN SIDDIQUEE M M, TAJBAKHSH N, et al. UNet++: a nested U-Net architecture for medical image segmentation[M]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer International Publishing, 2018: 3-11.
[49] LIU Xiaohong, LIU Yaojie, CHEN Jun, et al. PSCC-net: progressive spatio-channel correlation network for image manipulation detection and localization[J]. IEEE transactions on circuits and systems for video technology, 2022, 32(11): 7505-7517.
[50] WANG Jingdong, SUN Ke, CHENG Tianheng, et al. Deep high-resolution representation learning for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(10): 3349-3364.
[51] SALLOUM R, REN Yuzhuo, JAY KUO C C. Image splicing localization using a multi-task fully convolutional network(MFCN)[J]. Journal of visual communication and image representation, 2018, 51: 201-209.
[52] SUN Zhihao, JIANG Haoran, WANG Danding, et al. SAFL-net: semantic-agnostic feature learning network with auxiliary plugins for image manipulation detection[C]//2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 22367-22376.
[53] ZHOU Peng, CHEN B C, HAN Xintong, et al. Generate, segment, and refine: towards generic manipulation segmentation[J]. Proceedings of the AAAI conference on artificial intelligence, 2020, 34(7): 13058-13065.
[54] Ma Xiaochen, Du Bo, Liu Xianggen, et al. IML-ViT: Image manipulation localization by vision Transformer[EB/OL]. (2023-07-27)[2024-03-04]. https://arxiv.org/abs/2307.14863v1.
[55] ZENG Yuyuan, ZHAO Bowen, QIU Shanzhao, et al. Toward effective image manipulation detection with proposal contrastive learning[J]. IEEE transactions on circuits and systems for video technology, 2023, 33(9): 4703-4714.
[56] HAO Qixian, REN Ruyong, WANG Kai, et al. EC-Net: General image tampering localization network based on edge distribution guidance and contrastive learning[J]. Knowledge-based systems, 2024, 293: 111656.
[57] WU Haiwei, CHEN Yiming, ZHOU Jiantao. Rethinking image forgery detection via contrastive learning and unsupervised clustering[EB/OL]. (2023-08-18) [2024-03-04]. https://arxiv.org/abs/2308.09307v1.
[58] ZHOU Jizhe, MA Xiaochen, DU Xia, et al. Pre-training-free image manipulation localization through non-mutually exclusive contrastive learning[C]//2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 22289-22299.
[59] BARNI M, PHAN Q T, TONDI B. Copy move source-target disambiguation through multi-branch CNNs[J]. IEEE transactions on information forensics and security, 2020, 16: 1825-1840.
[60] ZHANG Ying, GOH J, WIN Lei Lei, et al. Image region forgery detection: a deep learning approach[C]//Proceedings of the Singapore Cyber-Security Conference 2016. Singapore: IOS Press, 2016: 1-11.
[61] ISLAM A, LONG Chengjiang, BASHARAT A, et al. DOA-GAN: dual-order attentive generative adversarial network for image copy-move forgery detection and localization[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 4676-4685.
[62] 石泽男, 陈海鹏, 张冬, 等. 预训练驱动的多模态边界感知视觉Transformer[J]. 软件学报, 2023, 34(5): 2051-2067.
SHI Zenan, CHEN Haipeng, ZHANG Dong, et al. Pre-training-driven multimodal boundary-aware vision Transformer[J]. Journal of software, 2023, 34(5): 2051-2067.
[63] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[EB/OL]. (2020-10-22) [2024-03-04]. https://arxiv.org/abs/2010.11929.
[64] LI Yanghao, XIE Saining, CHEN Xinlei, et al. Benchmarking detection transfer learning with vision Transformers[EB/OL]. (2021-11-22)[2024-03-04]. https://arxiv.org/abs/2111.11429v1.
[65] LI Yanghao, WU Chaoyuan, FAN Haoqi, et al. MViTv2: improved multiscale vision Transformers for classification and detection[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 4794-4804.
[66] LI Yuxi, CHENG Fuyuan, YU Wangbo, et al. AdaIFL: adaptive image forgery localization via a dynamic and importance-aware Transformer network[C]//Computer Vision–ECCV 2024. Cham: Springer Nature Switzerland, 2024: 477-493.
[67] LI Shuaibo, MA Wei, GUO Jianwei, et al. UnionFormer: unified-learning Transformer with multi-view representation for image manipulation detection and localization[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 12523-12533.
[68] ROZANTSEV A, SALZMANN M, FUA P. Beyond sharing weights for deep domain adaptation[J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 41(4): 801-814.
[69] ZHUANG Peiyu, LI Haodong, YANG Rui, et al. ReLoc: a restoration-assisted framework for robust image tampering localization[J]. IEEE transactions on information forensics and security, 2023, 18: 5243-5257.
[70] WU Haiwei, ZHOU Jiantao, TIAN Jinyu, et al. Robust image forgery detection over online social network shared images[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 13430-13439.
[71] HSU Y F, CHANG S F. Detecting image splicing using geometry invariants and camera characteristics consistency[C]//2006 IEEE International Conference on Multimedia and Expo. Toronto: IEEE, 2006: 549-552.
[72] AMERINI I, BALLAN L, CALDELLI R, et al. A SIFT-based forensic method for copy–move attack detection and transformation recovery[J]. IEEE transactions on information forensics and security, 2011, 6(3): 1099-1110.
[73] DONG Jing, WANG Wei, TAN Tieniu. CASIA image tampering detection evaluation database[C]//2013 IEEE China Summit and International Conference on Signal and Information Processing. Beijing: IEEE, 2013: 422-426.
[74] DE CARVALHO T J, RIESS C, ANGELOPOULOU E, et al. Exposing digital image forgeries by illumination color classification[J]. IEEE transactions on information forensics and security, 2013, 8(7): 1182-1194.
[75] TRALIC D, ZUPANCIC I, GRGIC S, et al. CoMoFoD: New database for copy-move forgery detection[C]//Proceedings ELMAR-2013. Zadar: IEEE, 2013: 49-54.
[76] SILVA E, CARVALHO T, FERREIRA A, et al. Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes[J]. Journal of visual communication and image representation, 2015, 29: 16-32.
[77] COZZOLINO D, POGGI G, VERDOLIVA L. Efficient dense-field copy–move forgery detection[J]. IEEE transactions on information forensics and security, 2015, 10(11): 2284-2297.
[78] WEN Bihan, ZHU Ye, SUBRAMANIAN R, et al. COVERAGE: a novel database for copy-move forgery detection[C]//2016 IEEE International Conference on Image Processing. Phoenix: IEEE, 2016: 161-165.
[79] ZAMPOGLOU M, PAPADOPOULOS S, KOMPATSIARIS Y. Detecting image splicing in the wild (WEB)[C]//2015 IEEE International Conference on Multimedia & Expo Workshops. Turin: IEEE, 2015: 1-6.
[80] KORUS P, HUANG Jiwu. Evaluation of random field models in multi-modal unsupervised tampering localization[C]//2016 IEEE International Workshop on Information Forensics and Security. Abu Dhabi: IEEE, 2016: 1-6.
[81] GUAN Haiying, KOZAK M, ROBERTSON E, et al. MFC datasets: large-scale benchmark datasets for media forensic challenge evaluation[C]//2019 IEEE Winter Applications of Computer Vision Workshops. Waikoloa Village: IEEE, 2019: 63-72.
[82] HELLER S, ROSSETTO L, SCHULDT H. The PS-Battles dataset—an image collection for image manipulation detection[EB/OL]. (2018-04-13)[2024-03-04]. https://arxiv.org/abs/1804.04866.
[83] MAHFOUDI G, TAJINI B, RETRAINT F, et al. DEFACTO: image and face manipulation dataset[C]//2019 27th European Signal Processing Conference. A Coruna: IEEE, 2019: 1-5.
[84] NOVOZAMSKY A, MAHDIAN B, SAIC S. IMD2020: a large-scale annotated dataset tailored for detecting manipulated images[C]//2020 IEEE Winter Applications of Computer Vision Workshops. Snowmass Village: IEEE, 2020: 71-80.
[85] WU Yue, ABD-ALMAGEED W, NATARAJAN P. BusterNet: detecting copy-move image forgery with source/target localization[C]//Computer Vision – ECCV 2018. Cham: Springer International Publishing, 2018: 170-186.
[86] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]//Computer Vision-ECCV 2014. Cham: Springer International Publishing, 2014: 740-755.
[87] XIAO Jianxiong, HAYS J, EHINGER K A, et al. SUN database: large-scale scene recognition from abbey to zoo[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE, 2010: 3485-3492.
[88] GLOE T, B?HME R, GLOE T, et al. The ‘Dresden Image Database’ for benchmarking digital image forensics[C]//Proceedings of the 2010 ACM Symposium on Applied Computing. Sierre: ACM, 2010: 1584-1590.
[89] ZHUANG Peiyu, LI Haodong, TAN Shunquan, et al. Image tampering localization using a dense fully convolutional network[J]. IEEE transactions on information forensics and security, 2021, 16: 2986-2999.
- 相似文献/References:
-
[1]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(2):193.[doi:10.3969/j.issn.1673-4785.201405060]
ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10():193.[doi:10.3969/j.issn.1673-4785.201405060]
[2]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(1):1.[doi:10.3969/j.issn.1673-4785.201403072]
DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10():1.[doi:10.3969/j.issn.1673-4785.201403072]
[3]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[4]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11():567.[doi:10.11992/tis.201511028]
[5]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11():728.[doi:10.11992/tis.201611021]
[6]王亚杰,邱虹坤,吴燕燕,等.计算机博弈的研究与发展[J].智能系统学报,2016,11(6):788.[doi:10.11992/tis.201609006]
WANG Yajie,QIU Hongkun,WU Yanyan,et al.Research and development of computer games[J].CAAI Transactions on Intelligent Systems,2016,11():788.[doi:10.11992/tis.201609006]
[7]黄心汉.A3I:21世纪科技之光[J].智能系统学报,2016,11(6):835.[doi:10.11992/tis.201605022]
HUANG Xinhan.A3I: the star of science and technology for the 21st century[J].CAAI Transactions on Intelligent Systems,2016,11():835.[doi:10.11992/tis.201605022]
[8]宋婉茹,赵晴晴,陈昌红,等.行人重识别研究综述[J].智能系统学报,2017,12(6):770.[doi:10.11992/tis.201706084]
SONG Wanru,ZHAO Qingqing,CHEN Changhong,et al.Survey on pedestrian re-identification research[J].CAAI Transactions on Intelligent Systems,2017,12():770.[doi:10.11992/tis.201706084]
[9]杨梦铎,栾咏红,刘文军,等.基于自编码器的特征迁移算法[J].智能系统学报,2017,12(6):894.[doi:10.11992/tis.201706037]
YANG Mengduo,LUAN Yonghong,LIU Wenjun,et al.Feature transfer algorithm based on an auto-encoder[J].CAAI Transactions on Intelligent Systems,2017,12():894.[doi:10.11992/tis.201706037]
[10]王科俊,赵彦东,邢向磊.深度学习在无人驾驶汽车领域应用的研究进展[J].智能系统学报,2018,13(1):55.[doi:10.11992/tis.201609029]
WANG Kejun,ZHAO Yandong,XING Xianglei.Deep learning in driverless vehicles[J].CAAI Transactions on Intelligent Systems,2018,13():55.[doi:10.11992/tis.201609029]
备注/Memo
收稿日期:2024-3-4。
基金项目:国家自然科学基金项目(62202024); 中央高校基本科研业务费专项资金项目(501QYJC2024139006).
作者简介:张汝波，教授，辽宁省教学名师，主要研究方向为智能机器人决策与控制技术。主持国家重点基础研究发展计划项目、国家高技术研究发展计划项目、国家自然科学基金项目等20余项。获得国家科学技术进步二等奖1项、省部级科学技术奖6项，获发明专利授权10余项。发表学术论文200余篇。E-mail：zhangrubo@dlnu.edu.cn;蔺庆龙，硕士研究生，主要研究方向为图像篡改检测。E-mail：linqinglong1999@163.com;张天一，助理教授，博士，主要研究方向为计算机视觉、机器学习以及图像视频内容安全分析，具体研究内容包括图像分割、目标检测、视频动作识别、深度学习、弱监督学习。主持国家自然科学基金项目1项、企事业单位委托项目1项。发表学术论文10余 篇。E-mail：zhang_tianyi@buaa.edu.cn。
通讯作者:张天一. E-mail：zhang_tianyi@buaa.edu.cn
更新日期/Last Update:
2025-03-05