{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T04:18:23Z","timestamp":1726978703900},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Signal Processing: Image Communication"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.image.2022.116695","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T20:23:05Z","timestamp":1649190185000},"page":"116695","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Hybrid deep-learning framework for object-based forgery detection in video"],"prefix":"10.1016","volume":"105","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7457-3691","authenticated-orcid":false,"given":"Shunquan","family":"Tan","sequence":"first","affiliation":[]},{"given":"Baoying","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jishen","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiwu","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.image.2022.116695_b1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/ACCESS.2013.2260814","article-title":"Information forensics: An overview of the first decade","volume":"1","author":"Stamm","year":"2013","journal-title":"IEEE Access"},{"key":"10.1016\/j.image.2022.116695_b2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.diin.2016.06.003","article-title":"Digital video tampering detection: An overview of passive techniques","volume":"18","author":"Sitara","year":"2016","journal-title":"Digit. Investig."},{"key":"10.1016\/j.image.2022.116695_b3","doi-asserted-by":"crossref","unstructured":"S. Chen, T. Sun, X. Jiang, P. He, S. Wang, Y.Q. Shi, Detecting double H.264 compression based on analyzing prediction residual distribution, in: Proc. 15th International Workshop on Digital Forensics and Watermarking (IWDW\u20192016), 2016, pp. 61\u201374.","DOI":"10.1007\/978-3-319-53465-7_5"},{"issue":"3","key":"10.1016\/j.image.2022.116695_b4","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1049\/iet-ifs.2015.0361","article-title":"Detecting multiple h.264\/AVC compressions with the same quantisation parameters","volume":"11","author":"Zhang","year":"2016","journal-title":"IET Inf. Secur."},{"key":"10.1016\/j.image.2022.116695_b5","doi-asserted-by":"crossref","unstructured":"S. Bian, W. Luo, J. Huang, Exposing video forgeries by detecting misaligned double compression, in: Proc. 1st International Conference on Video and Image Processing (ICVIP\u20192017), 2017, pp. 44\u201348.","DOI":"10.1145\/3177404.3177420"},{"key":"10.1016\/j.image.2022.116695_b6","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jvcir.2017.06.010","article-title":"Frame-wise detection of relocated I-frames in double compressed H.264 videos based on convolutional neural network","volume":"48","author":"He","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"1","key":"10.1016\/j.image.2022.116695_b7","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1109\/TIFS.2017.2745687","article-title":"Detection of double compression with the same coding parameters based on quality degradation mechanism analysis","volume":"13","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.image.2022.116695_b8","doi-asserted-by":"crossref","unstructured":"H. Liu, S. Li, S. Bian, Detecting frame deletion in H.264 video, in: Proc. 10th International Conference on Information Security Practice and Experience (ISPEC\u20192014), 2014, pp. 262\u2013270.","DOI":"10.1007\/978-3-319-06320-1_20"},{"key":"10.1016\/j.image.2022.116695_b9","doi-asserted-by":"crossref","unstructured":"A. Gironi, M. Fontani, T. Bianchi, A. Piva, M. Barni, A video forensic technique for detecting frame deletion and insertion, in: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u20192014), 2014, pp. 6226\u20136230.","DOI":"10.1109\/ICASSP.2014.6854801"},{"issue":"12","key":"10.1016\/j.image.2022.116695_b10","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1109\/TCSVT.2016.2593612","article-title":"Motion-adaptive frame deletion detection for digital video forensics","volume":"27","author":"Feng","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"24","key":"10.1016\/j.image.2022.116695_b11","doi-asserted-by":"crossref","first-page":"25767","DOI":"10.1007\/s11042-017-4762-2","article-title":"Inter-frame forgery detection in H.264 videos using motion and brightness gradients","volume":"76","author":"Kingra","year":"2017","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.image.2022.116695_b12","doi-asserted-by":"crossref","first-page":"25323","DOI":"10.1109\/ACCESS.2018.2819624","article-title":"Coarse-to-fine copy-move forgery detection for video forensics","volume":"6","author":"Jia","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.image.2022.116695_b13","doi-asserted-by":"crossref","unstructured":"C.C. Hsu, T.Y. Hung, C.W. Lin, C.T. Hsu, Video forgery detection using correlation of noise residue, in: Proc. IEEE 2008 International Workshop on Multimedia Signal Processing (MMSP\u201908), 2008, pp. 170\u2013174.","DOI":"10.1109\/MMSP.2008.4665069"},{"key":"10.1016\/j.image.2022.116695_b14","doi-asserted-by":"crossref","unstructured":"J. Zhang, Y. Su, M. Zhang, Exposing digital video forgery by ghost shadow artifact, in: Proc. 1st ACM Workshop on Multimedia in Forensics (MiFor\u201909), 2009, pp. 49\u201354.","DOI":"10.1145\/1631081.1631093"},{"key":"10.1016\/j.image.2022.116695_b15","doi-asserted-by":"crossref","unstructured":"A.V. Subramanyam, S. Emmanuel, Video forgery detection using HOG features and compression properties, in: Proc. IEEE 2012 International Workshop on Multimedia Signal Processing (MMSP\u201912), 2012, pp. 89\u201394.","DOI":"10.1109\/MMSP.2012.6343421"},{"issue":"1","key":"10.1016\/j.image.2022.116695_b16","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/TIFS.2011.2165843","article-title":"Exposing digital forgeries in ballistic motion","volume":"7","author":"Conotter","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"3","key":"10.1016\/j.image.2022.116695_b17","first-page":"164","article-title":"Detection of object-based manipulation by the statistical features of object contour","volume":"236","author":"Chen","year":"2014","journal-title":"Forensic Sci. Int."},{"issue":"11","key":"10.1016\/j.image.2022.116695_b18","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TCSVT.2015.2473436","article-title":"Automatic detection of object-based forgery in advanced video","volume":"26","author":"Chen","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.image.2022.116695_b19","doi-asserted-by":"crossref","unstructured":"T. Pevny, J. Fridrich, Merging Markov and DCT features for multi-class JPEG steganalysis, in: Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, Vol. 6505, 2007, pp. 301\u2013304.","DOI":"10.1117\/12.696774"},{"issue":"2","key":"10.1016\/j.image.2022.116695_b20","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TIFS.2010.2045842","article-title":"Steganalysis by subtractive pixel adjacency matrix","volume":"5","author":"Pevny","year":"2010","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"3","key":"10.1016\/j.image.2022.116695_b21","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TIFS.2012.2190402","article-title":"Rich models for steganalysis of digital images","volume":"7","author":"Fridrich","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"2","key":"10.1016\/j.image.2022.116695_b22","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TIFS.2011.2175919","article-title":"Ensemble classifiers for steganalysis of digital media","volume":"7","author":"Kodovsk\u00fd","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.image.2022.116695_b23","doi-asserted-by":"crossref","unstructured":"S. Tan, S. Chen, B. Li, GOP based automatic detection of object-based forgery in advanced video, in: Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA\u20192015), 2015, pp. 719\u2013722.","DOI":"10.1109\/APSIPA.2015.7415366"},{"key":"10.1016\/j.image.2022.116695_b24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"10.1016\/j.image.2022.116695_b25","doi-asserted-by":"crossref","unstructured":"S. Tan, B. Li, Stacked convolutional auto-encoders for steganalysis of digital images, in: Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA\u20192014), 2014, pp. 1\u20134.","DOI":"10.1109\/APSIPA.2014.7041565"},{"issue":"5","key":"10.1016\/j.image.2022.116695_b26","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/LSP.2016.2548421","article-title":"Structural design of convolutional neural networks for steganalysis","volume":"23","author":"Xu","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"issue":"11","key":"10.1016\/j.image.2022.116695_b27","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TIFS.2017.2710946","article-title":"Deep learning hierarchical representations for image steganalysis","volume":"12","author":"Ye","year":"2017","journal-title":"IEEE Trans. Inf. Forensics and Secur."},{"issue":"5","key":"10.1016\/j.image.2022.116695_b28","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1109\/TIFS.2017.2779446","article-title":"Large-scale JPEG steganalysis using hybrid deep-learning framework","volume":"13","author":"Zeng","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.image.2022.116695_b29","doi-asserted-by":"crossref","unstructured":"G. Xu, Deep convolutional neural network to detect J-UNIWARD, in: Proc. 5th ACM Information Hiding and Multimedia Security Workshop (IH&MMSec\u20192017), 2017, pp. 67\u201373.","DOI":"10.1145\/3082031.3083236"},{"issue":"5","key":"10.1016\/j.image.2022.116695_b30","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TIFS.2018.2871749","article-title":"Deep residual network for steganalysis of digital images","volume":"14","author":"Boroumand","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"10","key":"10.1016\/j.image.2022.116695_b31","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1109\/TIFS.2019.2904413","article-title":"WISERNet: WIder separate-then-reunion network for steganalysis of color images","volume":"14","author":"Zeng","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"11","key":"10.1016\/j.image.2022.116695_b32","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/LSP.2015.2438008","article-title":"Median filtering forensics based on convolutional neural networks","volume":"22","author":"Chen","year":"2015","journal-title":"IEEE Signal Process. Lett."},{"issue":"8","key":"10.1016\/j.image.2022.116695_b33","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1109\/TMM.2016.2571999","article-title":"Audio recapture detection with convolutional neural networks","volume":"18","author":"Lin","year":"2016","journal-title":"IEEE Trans. Multimed."},{"issue":"3","key":"10.1016\/j.image.2022.116695_b34","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1109\/LSP.2016.2641006","article-title":"First steps toward camera model identification with convolutional neural networks","volume":"24","author":"Bondi","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.image.2022.116695_b35","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.jvcir.2017.09.003","article-title":"Aligned and non-aligned double JPEG detection using convolutional neural networks","volume":"49","author":"Barni","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"1","key":"10.1016\/j.image.2022.116695_b36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3390\/sym10010003","article-title":"Deep learning for detection of object-based forgery in advanced video","volume":"10","author":"Yao","year":"2017","journal-title":"Symmetry"},{"key":"10.1016\/j.image.2022.116695_b37","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/OJSP.2021.3074298","article-title":"FOCAL: A forgery localization framework based on video coding self-consistency","volume":"2","author":"Verde","year":"2021","journal-title":"IEEE Open J. Signal Process."},{"issue":"1","key":"10.1016\/j.image.2022.116695_b38","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"10.1016\/j.image.2022.116695_b39","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"year":"2015","series-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","key":"10.1016\/j.image.2022.116695_b40"},{"issue":"8","key":"10.1016\/j.image.2022.116695_b41","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","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.image.2022.116695_b42","doi-asserted-by":"crossref","unstructured":"A. Graves, S. Fern\u00e1ndez, J. Schmidhuber, Bidirectional LSTM networks for improved phoneme classification and recognition, in: Duch, W\u0142odzis\u0142aw and Kacprzyk, Janusz and Oja, Erkki and Zadro\u017cny, S\u0142awomir (eds.) Proc. Artificial Neural Networks: Formal Models and their Applications (ICANN\u20192005), 2005, pp. 799\u2013804.","DOI":"10.1007\/11550907_126"},{"key":"10.1016\/j.image.2022.116695_b43","unstructured":"M. Abadi, et al. TensorFlow: A system for large-scale machine learning, in: Proc. 12th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201916), 2016,v pp. 265\u2013283."},{"issue":"10","key":"10.1016\/j.image.2022.116695_b44","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1109\/LSP.2017.2745572","article-title":"Automatic steganographic distortion learning using a generative adversarial network","volume":"24","author":"Tang","year":"2017","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Signal Processing: Image Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923596522000406?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923596522000406?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T17:09:12Z","timestamp":1726938552000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0923596522000406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":44,"alternative-id":["S0923596522000406"],"URL":"https:\/\/doi.org\/10.1016\/j.image.2022.116695","relation":{},"ISSN":["0923-5965"],"issn-type":[{"type":"print","value":"0923-5965"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hybrid deep-learning framework for object-based forgery detection in video","name":"articletitle","label":"Article Title"},{"value":"Signal Processing: Image Communication","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.image.2022.116695","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"116695"}}