{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T22:05:24Z","timestamp":1764194724577,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>As a popular technique for swapping faces with someone else\u2019s in images or videos through deep neural networks, deepfake causes a serious threat to the security of multimedia content today. However, because counterfeit images are usually compressed when propagating over the Internet, and because the compression factor used is unknown, most of the existing deepfake detection models have poor robustness for the detection of compressed images with unknown compression factors. To solve this problem, we notice that an image has a high similarity with its compressed image based on symmetry, and this similarity is not easily affected by the compression factor, so this similarity feature can be used as an important clue for compressed deepfake detection. A TCNSC (Two-branch Convolutional Networks with Similarity and Classifier) method that combines compression factor independence is proposed in this paper. The TCNSC method learns two feature representations from the deepfake image, i.e., similarity of the image and its compressed counterpart and authenticity of the deepfake image. A joint training strategy is then utilized for feature extraction, in which the similarity characteristics are obtained by similarity learning while obtaining authenticity characteristics, so the proposed TCNSC model is trained for robust feature learning. Experimental results on the FaceForensics++ (FF++) dataset show that the proposed method significantly outperforms all competing methods under three compression settings of high-quality (HQ), medium-quality (MQ), and low-quality (LQ). For the LQ, MQ, and HQ settings, TCNSC achieves 91.8%, 93.4%, and 95.3% in accuracy, and outperforms the state-of-art method (Xception-RAW) by 16.9%, 10.1%, and 4.1%, respectively.<\/jats:p>","DOI":"10.3390\/sym14122691","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T06:58:29Z","timestamp":1671433109000},"page":"2691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detecting Compressed Deepfake Images Using Two-Branch Convolutional Networks with Similarity and Classifier"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6424-4771","authenticated-orcid":false,"given":"Ping","family":"Chen","sequence":"first","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"School of Computer Information, Minnan Science and Technology University, Quanzhou 362300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9332-5258","authenticated-orcid":false,"given":"Ming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"Education Technology Center of Zhejiang Province, Hangzhou 310030, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"ref_1","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_2","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_3","unstructured":"Li, Y., and Lyu, S. (2018). Exposing deepfake videos by detecting face warping artifacts. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., and Guo, B. (2020, January 13\u201319). Face X-ray for more general face forgery detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., and Ji, R. (2021, January 2\u20139). Local relation learning for face forgery detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i2.16193"},{"key":"ref_6","unstructured":"Gu, Q., Chen, S., Yao, T., Chen, Y., Ding, S., and Yi, R. (March, January 22). Exploiting fine-grained face forgery clues via progressive enhancement learning. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., and Yu, N. (2021, January 20\u201325). Multi-attentional deepfake detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"ref_8","unstructured":"LIY, C.M., and InIctuOculi, L. (2018, January 11\u201313). ExposingAICreated FakeVideosbyDetectingEyeBlinking. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., and Lyu, S. (2019, January 12\u201317). Exposing deep fakes using inconsistent head poses. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nie\u00dfner, M. (2019, January 27\u201328). Faceforensics++: Learning to detect manipulated facial images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00009"},{"key":"ref_11","unstructured":"Li, Y., and Lyu, S. (2019, January 16\u201317). Exposing DeepFake Videos By Detecting Face Warping Artifacts. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Yamagishi, J., and Echizen, I. (2019, January 12\u201317). Capsule-forensics: Using capsule networks to detect forged images and videos. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682602"},{"key":"ref_15","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., and Echizen, I. (2018, January 11\u201313). Mesonet: A compact facial video forgery detection network. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China.","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"ref_17","unstructured":"Sun, K., Yao, T., Chen, S., Ding, S., Li, J., and Ji, R. (March, January 22). Dual contrastive learning for general face forgery detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually."},{"key":"ref_18","unstructured":"Frank, J., Eisenhofer, T., Sch\u00f6nherr, L., Fischer, A., Kolossa, D., and Holz, T. (2020, January 13\u201318). Leveraging frequency analysis for deep fake image recognition. Proceedings of the International Conference on Machine Learning, Virtually."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"83144","DOI":"10.1109\/ACCESS.2020.2988660","article-title":"Deepvision: Deepfakes detection using human eye blinking pattern","volume":"8","author":"Jung","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","unstructured":"Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., and Ma, L. (March, January 22). Delving into the local: Dynamic inconsistency learning for deepfake video detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually."},{"key":"ref_21","unstructured":"Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y. (2018). Learning deep representations by mutual information estimation and maximization. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_23","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, Virtually."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_26","unstructured":"(2022, February 24). Deepfakes. Available online: https:\/\/github.com\/deepfakes\/faceswap."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and Nie\u00dfner, M. (2016, January 27\u201330). Face2face: Real-time face capture and reenactment of rgb videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.262"},{"key":"ref_28","unstructured":"(2022, February 24). FaceSwap. Available online: https:\/\/github.com\/MarekKowalski\/FaceSwap."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3323035","article-title":"Deferred neural rendering: Image synthesis using neural textures","volume":"38","author":"Thies","year":"2019","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1109\/TIP.2017.2765830","article-title":"Multi-task convolutional neural network for pose-invariant face recognition","volume":"27","author":"Yin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1016\/j.image.2012.01.011","article-title":"Objective assessment of the WebP image coding algorithm","volume":"27","author":"Ginesu","year":"2012","journal-title":"Signal Process. Image Commun."},{"key":"ref_32","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_34","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_35","unstructured":"Durall, R., Keuper, M., Pfreundt, F.J., and Keuper, J. (2019). Unmasking deepfakes with simple features. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Durall, R., Keuper, M., and Keuper, J. (2020, January 14\u201319). Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00791"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/12\/2691\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:44:08Z","timestamp":1760147048000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/12\/2691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,19]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["sym14122691"],"URL":"https:\/\/doi.org\/10.3390\/sym14122691","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2022,12,19]]}}}