{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:22:49Z","timestamp":1776205369274,"version":"3.50.1"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T00:00:00Z","timestamp":1734739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62071142"],"award-info":[{"award-number":["62071142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024A1515012299"],"award-info":[{"award-number":["2024A1515012299"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Science and Technology Program","award":["ZDSYS20210623091809029"],"award-info":[{"award-number":["ZDSYS20210623091809029"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>Image splicing is a widely occurrence image tampering technology. With the rapid development of digital image processing technology, detecting image splicing forgery has become significantly challenging. Although various methods have been devised to identify such tampered images, existing approaches have not achieved optimal performance due to limitations in effectively leveraging feature maps of different scales. To address this issue, we propose a novel method for image splicing forgery detection called multi-scale feature attention fusion network (MFAF-Net). We propose a multi-scale atrous feature attention (MAFA) module designed to capture rich contextual features for multi-scale high-level feature fusion. Additionally, we present the multi-branch attention mechanism (MBAM) module to fuse contextual information from various branches for low-level features. This integration enhances the capability of low-level features to produce more refined pixel-level attention. We employ the weighted binary cross-entropy loss and dice loss in the MFAF-Net to overcome the imbalance between positive and negative samples. Extensive experiments demonstrate that the proposed MFAF-Net outperforms state-of-the-art methods. Robustness experiments also show our model exhibits image splicing forgery detection robustness under common attacks.<\/jats:p>","DOI":"10.1145\/3698770","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T15:29:47Z","timestamp":1728314987000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-Scale Feature Attention Fusion for Image Splicing Forgery Detection"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9138-5393","authenticated-orcid":false,"given":"Enji","family":"Liang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6320-9096","authenticated-orcid":false,"given":"Kuiyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3529-0541","authenticated-orcid":false,"given":"Zhongyun","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8702-8302","authenticated-orcid":false,"given":"Xiaohua","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China and School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,12,21]]},"reference":[{"issue":"7","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1109\/TIP.2019.2895466","article-title":"Hybrid LSTM and encoder\u2013decoder architecture for detection of image forgeries","volume":"28","author":"Bappy Jawadul H.","year":"2019","unstructured":"Jawadul H. Bappy, Cody Simons, Lakshmanan Nataraj, B. S. Manjunath, and Amit K. Roy-Chowdhury. 2019. Hybrid LSTM and encoder\u2013decoder architecture for detection of image forgeries. IEEE Transactions on Image Processing 28, 7 (2019), 3286\u20133300.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TIFS.2012.2187516","article-title":"Image forgery localization via block-grained analysis of JPEG artifacts","volume":"7","author":"Bianchi Tiziano","year":"2012","unstructured":"Tiziano Bianchi and Alessandro Piva. 2012. Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Transactions on Information Forensics and Security 7, 3 (2012), 1003\u20131017.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"4","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1109\/TIFS.2009.2033749","article-title":"Accurate detection of demosaicing regularity for digital image forensics","volume":"4","author":"Cao Hong","year":"2009","unstructured":"Hong Cao and Alex C. Kot. 2009. Accurate detection of demosaicing regularity for digital image forensics. IEEE Transactions on Information Forensics and Security 4, 4 (2009), 899\u2013910.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"2","key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/TCSVT.2022.3204753","article-title":"SNIS: A signal noise separation-based network for post-processed image forgery detection","volume":"33","author":"Chen Jiaxin","year":"2023","unstructured":"Jiaxin Chen, Xin Liao, Wei Wang, Zhenxing Qian, Zheng Qin, and Yaonan Wang. 2023. SNIS: A signal noise separation-based network for post-processed image forgery detection. IEEE Transactions on Circuits and Systems for Video Technology 33, 2 (2023), 935\u2013951.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"2","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1109\/TIFS.2011.2106121","article-title":"Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection","volume":"6","author":"Chen Yi-Lei","year":"2011","unstructured":"Yi-Lei Chen and Chiou-Ting Hsu. 2011. Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection. IEEE Transactions on Information Forensics and Security 6, 2 (2011), 396\u2013406.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"De Boer Pieter-Tjerk","year":"2005","unstructured":"Pieter-Tjerk De Boer, Dirk P. Kroese, Shie Mannor, and Reuven Y. Rubinstein. 2005. A tutorial on the cross-entropy method. Annals of Operations Research 134 (2005), 19\u201367.","journal-title":"Annals of Operations Research"},{"key":"e_1_3_1_8_2","first-page":"422","volume-title":"Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing","author":"Dong Jing","year":"2013","unstructured":"Jing Dong, Wei Wang, and Tieniu Tan. 2013. Casia image tampering detection evaluation database. In Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing. IEEE, 422\u2013426."},{"issue":"5","key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/TIFS.2012.2202227","article-title":"Image forgery localization via fine-grained analysis of CFA artifacts","volume":"7","author":"Ferrara Pasquale","year":"2012","unstructured":"Pasquale Ferrara, Tiziano Bianchi, Alessia De Rosa, and Alessandro Piva. 2012. Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Transactions on Information Forensics and Security 7, 5 (2012), 1566\u20131577.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"2","key":"e_1_3_1_10_2","first-page":"652","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao Shang-Hua","year":"2019","unstructured":"Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, and Philip Torr. 2019. Res2net: A new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 2 (2019), 652\u2013662.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/WACVW.2019.00018","article-title":"MFC datasets: Large-scale benchmark datasets for media forensic challenge evaluation","author":"Guan Haiying","year":"2019","unstructured":"Haiying Guan, Mark Kozak, Eric Robertson, Yooyoung Lee, Amy N. Yates, Andrew Delgado, Daniel Zhou, Timothee Kheyrkhah, Jeff Smith, and Jonathan Fiscus. 2019. MFC datasets: Large-scale benchmark datasets for media forensic challenge evaluation. In Proceedings of the 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). IEEE, 63\u201372.","journal-title":"Proceedings of the 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)"},{"key":"e_1_3_1_12_2","first-page":"15055","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Hao Jing","year":"2021","unstructured":"Jing Hao, Zhixin Zhang, Shicai Yang, Di Xie, and Shiliang Pu. 2021. TransForensics: Image forgery localization with dense self-attention. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. IEEE, 15055\u201315064."},{"issue":"8","key":"e_1_3_1_13_2","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735\u20131780.","journal-title":"Neural Computation"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/ICME.2006.262447","volume-title":"Proceedings of the 2006 IEEE International Conference on Multimedia and Expo","author":"Hsu Yu-Feng","year":"2006","unstructured":"Yu-Feng Hsu and Shih-Fu Chang. 2006. Detecting image splicing using geometry invariants and camera characteristics consistency. In Proceedings of the 2006 IEEE International Conference on Multimedia and Expo. IEEE, 549\u2013552."},{"key":"e_1_3_1_15_2","volume-title":"Advances in Neural Information Processing Systems","author":"Hu Jie","year":"2018","unstructured":"Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Andrea Vedaldi. 2018. Gather-excite: Exploiting feature context in convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 31. Curran Associates, Inc."},{"key":"e_1_3_1_16_2","first-page":"7132","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hu Jie","year":"2018","unstructured":"Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 7132\u20137141."},{"key":"e_1_3_1_17_2","first-page":"312","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV)","author":"Hu Xuefeng","year":"2020","unstructured":"Xuefeng Hu, Zhihan Zhang, Zhenye Jiang, Syomantak Chaudhuri, Zhenheng Yang, and Ram Nevatia. 2020. SPAN: Spatial pyramid attention network for image manipulation localization. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, 312\u2013328."},{"key":"e_1_3_1_18_2","first-page":"4676","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Islam Ashraful","year":"2020","unstructured":"Ashraful Islam, Chengjiang Long, Arslan Basharat, and Anthony Hoogs. 2020. DOA-GAN: Dual-order attentive generative adversarial network for image copy-move forgery detection and localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 4676\u20134685."},{"issue":"3","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TIFS.2007.903848","article-title":"Exposing digital forgeries in complex lighting environments","volume":"2","author":"Johnson Micah K.","year":"2007","unstructured":"Micah K. Johnson and Hany Farid. 2007. Exposing digital forgeries in complex lighting environments. IEEE Transactions on Information Forensics and Security 2, 3 (2007), 450\u2013461.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_1_20_2","volume-title":"Advances in Neural Information Processing Systems","author":"Kniaz Vladimir V.","year":"2019","unstructured":"Vladimir V. Kniaz, Vladimir Knyaz, and Fabio Remondino. 2019. The point where reality meets fantasy: Mixed adversarial generators for image splice detection. In Advances in Neural Information Processing Systems, Vol. 32. Curran Associates, Inc."},{"issue":"4","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1109\/TIFS.2010.2074194","article-title":"Detecting forgery from static-scene video based on inconsistency in noise level functions","volume":"5","author":"Kobayashi Michihiro","year":"2010","unstructured":"Michihiro Kobayashi, Takahiro Okabe, and Yoichi Sato. 2010. Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Transactions on Information Forensics and Security 5, 4 (2010), 883\u2013892.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"2","key":"e_1_3_1_22_2","first-page":"2","article-title":"A picture\u2019s worth","volume":"6","author":"Krawetz Neal","year":"2007","unstructured":"Neal Krawetz and Hacker Factor Solutions. 2007. A picture\u2019s worth. Hacker Factor Solutions 6, 2 (2007), 2.","journal-title":"Hacker Factor Solutions"},{"issue":"8","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1007\/s11263-022-01617-5","article-title":"Learning JPEG compression artifacts for image manipulation detection and localization","volume":"130","author":"Kwon Myung-Joon","year":"2022","unstructured":"Myung-Joon Kwon, Seung-Hun Nam, In-Jae Yu, Heung-Kyu Lee, and Changick Kim. 2022. Learning JPEG compression artifacts for image manipulation detection and localization. International Journal of Computer Vision 130, 8 (2022), 1875\u20131895.","journal-title":"International Journal of Computer Vision"},{"issue":"4","key":"e_1_3_1_24_2","first-page":"4430","article-title":"DS-Net++: Dynamic weight slicing for efficient inference in CNNs and vision transformers","volume":"45","author":"Li Changlin","year":"2022","unstructured":"Changlin Li, Guangrun Wang, Bing Wang, Xiaodan Liang, Zhihui Li, and Xiaojun Chang. 2022. DS-Net++: Dynamic weight slicing for efficient inference in CNNs and vision transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 4 (2022), 4430\u20134446.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"8","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"10555","DOI":"10.1109\/TPAMI.2023.3257546","article-title":"When object detection meets knowledge distillation: A survey","volume":"45","author":"Li Zhihui","year":"2023","unstructured":"Zhihui Li, Pengfei Xu, Xiaojun Chang, Luyao Yang, Yuanyuan Zhang, Lina Yao, and Xiaojiang Chen. 2023. When object detection meets knowledge distillation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 8 (2023), 10555\u201310579.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1109\/JSTSP.2020.3002391","article-title":"Robust detection of image operator chain with two-stream convolutional neural network","volume":"14","author":"Liao Xin","year":"2020","unstructured":"Xin Liao, Kaide Li, Xinshan Zhu, and K. J. Ray Liu. 2020. Robust detection of image operator chain with two-stream convolutional neural network. IEEE Journal of Selected Topics in Signal Processing 14, 5 (2020), 955\u2013968.","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"issue":"11","key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.1016\/j.patcog.2009.03.019","article-title":"Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis","volume":"42","author":"Lin Zhouchen","year":"2009","unstructured":"Zhouchen Lin, Junfeng He, Xiaoou Tang, and Chi-Keung Tang. 2009. Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognition 42, 11 (2009), 2492\u20132501.","journal-title":"Pattern Recognition"},{"issue":"11","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"7505","DOI":"10.1109\/TCSVT.2022.3189545","article-title":"PSCC-Net: Progressive spatio-channel correlation network for image manipulation detection and localization","volume":"32","author":"Liu Xiaohong","year":"2022","unstructured":"Xiaohong Liu, Yaojie Liu, Jun Chen, and Xiaoming Liu. 2022. PSCC-Net: Progressive spatio-channel correlation network for image manipulation detection and localization. IEEE Transactions on Circuits and Systems for Video Technology 32, 11 (2022), 7505\u20137517.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_1_29_2","first-page":"3431","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Long Jonathan","year":"2015","unstructured":"Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 3431\u20133440."},{"issue":"3","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TIFS.2010.2051426","article-title":"JPEG error analysis and its applications to digital image forensics","volume":"5","author":"Luo Weiqi","year":"2010","unstructured":"Weiqi Luo, Jiwu Huang, and Guoping Qiu. 2010. JPEG error analysis and its applications to digital image forensics. IEEE Transactions on Information Forensics and Security 5, 3 (2010), 480\u2013491.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/s11263-013-0688-y","article-title":"Exposing region splicing forgeries with blind local noise estimation","volume":"110","author":"Lyu Siwei","year":"2014","unstructured":"Siwei Lyu, Xunyu Pan, and Xing Zhang. 2014. Exposing region splicing forgeries with blind local noise estimation. International Journal of Computer Vision 110 (2014), 202\u2013221.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_1_32_2","first-page":"546","volume-title":"Proceedings of the 2008 International Conference on Computational Sciences and Its Applications","author":"Mahdian Babak","year":"2008","unstructured":"Babak Mahdian and Stanislav Saic. 2008. Detection of resampling supplemented with noise inconsistencies analysis for image forensics. In Proceedings of the 2008 International Conference on Computational Sciences and Its Applications. IEEE, 546\u2013556."},{"issue":"10","key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.imavis.2009.02.001","article-title":"Using noise inconsistencies for blind image forensics","volume":"27","author":"Mahdian Babak","year":"2009","unstructured":"Babak Mahdian and Stanislav Saic. 2009. Using noise inconsistencies for blind image forensics. Image and Vision Computing 27, 10 (2009), 1497\u20131503.","journal-title":"Image and Vision Computing"},{"key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"133488","DOI":"10.1109\/ACCESS.2020.3009877","article-title":"A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection","volume":"8","author":"Marra Francesco","year":"2020","unstructured":"Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi. 2020. A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection. IEEE Access 8 (2020), 133488\u2013133502.","journal-title":"IEEE Access"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/3DV.2016.79","volume-title":"Proceedings of the 2016 4th International Conference on 3D Vision (3DV)","author":"Milletari Fausto","year":"2016","unstructured":"Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 4th International Conference on 3D Vision (3DV). IEEE, 565\u2013571."},{"key":"e_1_3_1_36_2","first-page":"2204","volume-title":"Advances in Neural Information Processing Systems","author":"Mnih Volodymyr","year":"2014","unstructured":"Volodymyr Mnih, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu. 2014. Recurrent models of visual attention. In Advances in Neural Information Processing Systems, Vol. 27. Curran Associates, Inc., 2204\u20132212."},{"issue":"10","key":"e_1_3_1_37_2","doi-asserted-by":"crossref","first-page":"3948","DOI":"10.1109\/TSP.2005.855406","article-title":"Exposing digital forgeries in color filter array interpolated images","volume":"53","author":"Popescu Alin C.","year":"2005","unstructured":"Alin C. Popescu and Hany Farid. 2005. Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing 53, 10 (2005), 3948\u20133959.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"e_1_3_1_38_2","first-page":"783","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Qin Zequn","year":"2021","unstructured":"Zequn Qin, Pengyi Zhang, Fei Wu, and Xi Li. 2021. FcaNet: Frequency channel attention networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. IEEE, 783\u2013792."},{"key":"e_1_3_1_39_2","first-page":"247","volume-title":"Proceedings of the Information Hiding: 11th International Workshop, IH \u201909","author":"Qu Zhenhua","year":"2009","unstructured":"Zhenhua Qu, Guoping Qiu, and Jiwu Huang. 2009. Detect digital image splicing with visual cues. In Proceedings of the Information Hiding: 11th International Workshop, IH \u201909. Springer, 247\u2013261."},{"issue":"3","key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2857069","article-title":"Forensic analysis of linear and nonlinear image filtering using quantization noise","volume":"12","author":"Ravi Hareesh","year":"2016","unstructured":"Hareesh Ravi, A. Venkata Subramanyam, and Sabu Emmanuel. 2016. Forensic analysis of linear and nonlinear image filtering using quantization noise. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 12, 3 (2016), 1\u201323.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"key":"e_1_3_1_41_2","first-page":"779","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Redmon Joseph","year":"2016","unstructured":"Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 779\u2013788."},{"issue":"15","key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"18219","DOI":"10.1007\/s10489-022-04421-3","article-title":"Multi-scale attention context-aware network for detection and localization of image splicing: efficient and robust identification network","volume":"53","author":"Ren Ruyong","year":"2023","unstructured":"Ruyong Ren, Shaozhang Niu, Junfeng Jin, Jiwei Zhang, Hua Ren, and Xiaojie Zhao. 2023. Multi-scale attention context-aware network for detection and localization of image splicing: efficient and robust identification network. Applied Intelligence 53, 15 (2023), 18219\u201318238.","journal-title":"Applied Intelligence"},{"issue":"4","key":"e_1_3_1_43_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3506853","article-title":"ESRNet: Efficient search and recognition network for image manipulation detection","volume":"18","author":"Ren Ruyong","year":"2022","unstructured":"Ruyong Ren, Shaozhang Niu, Hua Ren, Shubin Zhang, Tengyue Han, and Xiaohai Tong. 2022. ESRNet: Efficient search and recognition network for image manipulation detection. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 18, 4 (2022), 1\u201323.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"key":"e_1_3_1_44_2","first-page":"1","article-title":"VL-BERT: Pre-training of generic Visual-Linguistic Representations","author":"Su Weijie","year":"2020","unstructured":"Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. 2020. VL-BERT: Pre-training of generic Visual-Linguistic Representations. In Proceedings of the International Conference on Learning Representations, 1\u201313.","journal-title":"Proceedings of the International Conference on Learning Representations"},{"key":"e_1_3_1_45_2","volume-title":"Advances in Neural Information Processing Systems","author":"Sutton Richard S.","year":"1999","unstructured":"Richard S. Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems, Vol. 12. MIT Press."},{"key":"e_1_3_1_46_2","first-page":"2364","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Junke","year":"2022","unstructured":"Junke Wang, Zuxuan Wu, Jingjing Chen, Xintong Han, Abhinav Shrivastava, Ser-Nam Lim, and Yu-Gang Jiang. 2022. Objectformer for image manipulation detection and localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2364\u20132373."},{"key":"e_1_3_1_47_2","first-page":"1257","volume-title":"Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP)","author":"Wang Wei","year":"2009","unstructured":"Wei Wang, Jing Dong, and Tieniu Tan. 2009. Effective image splicing detection based on image chroma. In Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE, 1257\u20131260."},{"issue":"4","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3572916","article-title":"Semi-supervised learning for Mars imagery classification and segmentation","volume":"19","author":"Wang Wenjing","year":"2023","unstructured":"Wenjing Wang, Lilang Lin, Zejia Fan, and Jiaying Liu. 2023. Semi-supervised learning for Mars imagery classification and segmentation. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 19, 4 (2023), 1\u201323.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"issue":"4","key":"e_1_3_1_49_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3408299","article-title":"Controlling neural learning network with multiple scales for image splicing forgery detection","volume":"16","author":"Wei Yang","year":"2020","unstructured":"Yang Wei, Zhuzhu Wang, Bin Xiao, Ximeng Liu, Zheng Yan, and Jianfeng Ma. 2020. Controlling neural learning network with multiple scales for image splicing forgery detection. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 4 (2020), 1\u201322.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"issue":"2","key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3183518","article-title":"Contrast enhancement estimation for digital image forensics","volume":"14","author":"Wen Longyin","year":"2018","unstructured":"Longyin Wen, Honggang Qi, and Siwei Lyu. 2018. Contrast enhancement estimation for digital image forensics. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 2 (2018), 1\u201321.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1109\/TIFS.2022.3144878","article-title":"Robust image forgery detection against transmission over online social networks","volume":"17","author":"Wu Haiwei","year":"2022","unstructured":"Haiwei Wu, Jiantao Zhou, Jinyu Tian, Jun Liu, and Yu Qiao. 2022. Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security 17 (2022), 443\u2013456.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_1_52_2","first-page":"9543","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wu Yue","year":"2019","unstructured":"Yue Wu, Wael AbdAlmageed, and Premkumar Natarajan. 2019. ManTra-Net: Manipulation tracing network for detection and localization of image forgeries with anomalous features. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 9543\u20139552."},{"key":"e_1_3_1_53_2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.ins.2019.09.038","article-title":"Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering","volume":"511","author":"Xiao Bin","year":"2020","unstructured":"Bin Xiao, Yang Wei, Xiuli Bi, Weisheng Li, and Jianfeng Ma. 2020. Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Information Sciences 511 (2020), 172\u2013191.","journal-title":"Information Sciences"},{"key":"e_1_3_1_54_2","first-page":"11794","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yang Zongxin","year":"2020","unstructured":"Zongxin Yang, Linchao Zhu, Yu Wu, and Yi Yang. 2020. Gated channel transformation for visual recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 11794\u201311803."},{"key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"12457","DOI":"10.1007\/s11042-016-3660-3","article-title":"Detecting image splicing based on noise level inconsistency","volume":"76","author":"Yao Heng","year":"2017","unstructured":"Heng Yao, Shuozhong Wang, Xinpeng Zhang, Chuan Qin, and Jinwei Wang. 2017. Detecting image splicing based on noise level inconsistency. Multimedia Tools and Applications 76 (2017), 12457\u201312479.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_1_56_2","first-page":"12","volume-title":"Proceedings of the 2007 IEEE International Conference on Multimedia and Expo","author":"Ye Shuiming","year":"2007","unstructured":"Shuiming Ye, Qibin Sun, and Ee-Chien Chang. 2007. Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In Proceedings of the 2007 IEEE International Conference on Multimedia and Expo. IEEE, 12\u201315."},{"key":"e_1_3_1_57_2","first-page":"7151","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhang Hang","year":"2018","unstructured":"Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal. 2018. Context encoding for semantic segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 7151\u20137160."},{"issue":"1","key":"e_1_3_1_58_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3513133","article-title":"Towards accurate oriented object detection in aerial images with adaptive multi-level feature fusion","volume":"19","author":"Zhen Peining","year":"2023","unstructured":"Peining Zhen, Shuqi Wang, Suming Zhang, Xiaotao Yan, Wei Wang, Zhigang Ji, and Hai-Bao Chen. 2023. Towards accurate oriented object detection in aerial images with adaptive multi-level feature fusion. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 19, 1 (2023), 1\u201322.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"key":"e_1_3_1_59_2","first-page":"1053","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhou Peng","year":"2018","unstructured":"Peng Zhou, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2018. Learning rich features for image manipulation detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 1053\u20131061."},{"key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1109\/TIFS.2022.3152362","article-title":"Self-adversarial training incorporating forgery attention for image forgery localization","volume":"17","author":"Zhuo Long","year":"2022","unstructured":"Long Zhuo, Shunquan Tan, Bin Li, and Jiwu Huang. 2022. Self-adversarial training incorporating forgery attention for image forgery localization. IEEE Transactions on Information Forensics and Security 17 (2022), 819\u2013834.","journal-title":"IEEE Transactions on Information Forensics and Security"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698770","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:09:44Z","timestamp":1750295384000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,21]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,31]]}},"alternative-id":["10.1145\/3698770"],"URL":"https:\/\/doi.org\/10.1145\/3698770","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,21]]},"assertion":[{"value":"2024-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}