{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T22:39:19Z","timestamp":1764542359522,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006477","name":"National Taiwan University","doi-asserted-by":"publisher","award":["NTU-112L900902","MOST 111-2221-E-002-134-MY3"],"award-info":[{"award-number":["NTU-112L900902","MOST 111-2221-E-002-134-MY3"]}],"id":[{"id":"10.13039\/501100006477","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Minister of Science and Technology, Taiwan","award":["NTU-112L900902","MOST 111-2221-E-002-134-MY3"],"award-info":[{"award-number":["NTU-112L900902","MOST 111-2221-E-002-134-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In the era of deepfakes and AI-generated content, digital image manipulation poses significant challenges to image authenticity, creating doubts about the credibility of images. Traditional image forensics techniques often struggle to detect sophisticated tampering, and passive detection approaches are reactive, verifying authenticity only after counterfeiting occurs. In this paper, we propose a novel full-resolution secure learned image codec (SLIC) designed to proactively prevent image manipulation by creating self-destructive artifacts upon re-compression. Once a sensitive image is encoded using SLIC, any subsequent re-compression or editing attempts will result in visually severe distortions, making the image\u2019s tampering immediately evident. Because the content of an SLIC image is either original or visually damaged after tampering, images encoded with this secure codec hold greater credibility. SLIC leverages adversarial training to fine-tune a learned image codec that introduces out-of-distribution perturbations, ensuring that the first compressed image retains high quality while subsequent re-compressions degrade drastically. We analyze and compare the adversarial effects of various perceptual quality metrics combined with different learned codecs. Our experiments demonstrate that SLIC holds significant promise as a proactive defense strategy against image manipulation, offering a new approach to enhancing image credibility and authenticity in a media landscape increasingly dominated by AI-driven forgeries.<\/jats:p>","DOI":"10.3390\/bdcc9010014","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T09:01:02Z","timestamp":1736931662000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Secure Learned Image Codec for Authenticity Verification via Self-Destructive Compression"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4352-0375","authenticated-orcid":false,"given":"Chen-Hsiu","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3631-1551","authenticated-orcid":false,"given":"Ja-Ling","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","article-title":"Deepfakes and beyond: A survey of face manipulation and fake detection","volume":"64","author":"Tolosana","year":"2020","journal-title":"Inf. Fusion"},{"doi-asserted-by":"crossref","unstructured":"Piva, A. (2013). An overview on image forensics. Int. Sch. Res. Not., 2013.","key":"ref_2","DOI":"10.1155\/2013\/496701"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17521","DOI":"10.1007\/s11042-022-13797-w","article-title":"Image forgery detection: A survey of recent deep-learning approaches","volume":"82","author":"Zanardelli","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_4","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","year":"2009","journal-title":"Image Vis. Comput."},{"doi-asserted-by":"crossref","unstructured":"Bayram, S., Sencar, H.T., and Memon, N. (2009, January 19\u201324). An efficient and robust method for detecting copy-move forgery. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan.","key":"ref_5","DOI":"10.1109\/ICASSP.2009.4959768"},{"unstructured":"Ghosh, A., Zhong, Z., Boult, T.E., and Singh, M. (2019, January 16\u201320). SpliceRadar: A Learned Method For Blind Image Forensics. Proceedings of the CVPR Workshops, Long Beach, CA, USA.","key":"ref_6"},{"key":"ref_7","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","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"doi-asserted-by":"crossref","unstructured":"Mahdian, B., and Saic, S. (2009, January 3). Detecting double compressed JPEG images. Proceedings of the 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), London, UK.","key":"ref_8","DOI":"10.1049\/ic.2009.0240"},{"doi-asserted-by":"crossref","unstructured":"Park, J., Cho, D., Ahn, W., and Lee, H.K. (2018, January 8\u201314). Double JPEG detection in mixed JPEG quality factors using deep convolutional neural network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","key":"ref_9","DOI":"10.1007\/978-3-030-01228-1_39"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/30.267415","article-title":"The trustworthy digital camera: Restoring credibility to the photographic image","volume":"39","author":"Friedman","year":"1993","journal-title":"IEEE Trans. Consum. Electron."},{"unstructured":"Blythe, P., and Fridrich, J. (2004). Secure digital camera. Digit. Investig., Available online: https:\/\/dfrws.org\/presentation\/secure-digital-camera\/.","key":"ref_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1109\/5.771070","article-title":"Digital watermarking for telltale tamper proofing and authentication","volume":"87","author":"Kundur","year":"1999","journal-title":"Proc. IEEE"},{"doi-asserted-by":"crossref","unstructured":"Lu, C.S., and Liao, H.Y.M. (November, January 30). Structural digital signature for image authentication: An incidental distortion resistant scheme. Proceedings of the 2000 ACM Workshops on Multimedia, Los Angeles, CA, USA.","key":"ref_13","DOI":"10.1145\/357744.357893"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3083","DOI":"10.1109\/TAI.2023.3340982","article-title":"Manipulation Attacks on Learned Image Compression","volume":"5","author":"Liu","year":"2023","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7842","DOI":"10.1109\/TCSVT.2023.3276442","article-title":"Towards robust neural image compression: Adversarial attack and model finetuning","volume":"33","author":"Chen","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"doi-asserted-by":"crossref","unstructured":"Huang, C.H., and Wu, J.L. (2024, January 3\u20136). SLIC: Secure Learned Image Codec through Compressed Domain Watermarking to Defend Image Manipulation. Proceedings of the 6th ACM International Conference on Multimedia in Asia, Auckland, New Zealand.","key":"ref_16","DOI":"10.1145\/3696409.3700161"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/S1110865702204047","article-title":"A survey of watermarking algorithms for image authentication","volume":"2002","author":"Rey","year":"2002","journal-title":"EURASIP J. Adv. Signal Process."},{"doi-asserted-by":"crossref","unstructured":"Ruiz, N., Bargal, S.A., and Sclaroff, S. (2020). Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems. arXiv.","key":"ref_18","DOI":"10.1007\/978-3-030-66823-5_14"},{"doi-asserted-by":"crossref","unstructured":"Lv, L. (2021, January 23\u201326). Smart watermark to defend against deepfake image manipulation. Proceedings of the 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), Chengdu, China.","key":"ref_19","DOI":"10.1109\/ICCCS52626.2021.9449287"},{"doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, R., Yu, J., Xu, Y., Li, W., and Zhang, J. (2024, January 16\u201322). Editguard: Versatile image watermarking for tamper localization and copyright protection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","key":"ref_20","DOI":"10.1109\/CVPR52733.2024.01137"},{"doi-asserted-by":"crossref","unstructured":"Yu, N., Skripniuk, V., Abdelnabi, S., and Fritz, M. (2021, January 10\u201317). Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","key":"ref_21","DOI":"10.1109\/ICCV48922.2021.01418"},{"key":"ref_22","first-page":"43","article-title":"Invisible Adversarial Watermarking: A Novel Security Mechanism for Enhancing Copyright Protection","volume":"21","author":"Wang","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1109\/TCSVT.2022.3207008","article-title":"Self-recoverable adversarial examples: A new effective protection mechanism in social networks","volume":"33","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"unstructured":"Ball\u00e9, J., Minnen, D., Singh, S., Hwang, S.J., and Johnston, N. (2018). Variational image compression with a scale hyperprior. arXiv.","key":"ref_24"},{"key":"ref_25","first-page":"10771","article-title":"Joint autoregressive and hierarchical priors for learned image compression","volume":"31","author":"Minnen","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"doi-asserted-by":"crossref","unstructured":"Cheng, Z., Sun, H., Takeuchi, M., and Katto, J. (2020, January 13\u201319). Learned image compression with discretized gaussian mixture likelihoods and attention modules. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","key":"ref_26","DOI":"10.1109\/CVPR42600.2020.00796"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1109\/TCSVT.2021.3089491","article-title":"Causal contextual prediction for learned image compression","volume":"32","author":"Guo","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1109\/TCSVT.2019.2910119","article-title":"Image and video compression with neural networks: A review","volume":"30","author":"Ma","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Mandt, S., and Theis, L. (2022). An introduction to neural data compression. arXiv.","key":"ref_29","DOI":"10.1561\/9781638281757"},{"doi-asserted-by":"crossref","unstructured":"Huang, C.H., and Wu, J.L. (2024). Unveiling the Future of Human and Machine Coding: A Survey of End-to-End Learned Image Compression. Entropy, 26.","key":"ref_30","DOI":"10.20944\/preprints202403.1272.v1"},{"doi-asserted-by":"crossref","unstructured":"Kim, J.H., Jang, S., Choi, J.H., and Lee, J.S. (2020, January 12\u201316). Instability of successive deep image compression. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","key":"ref_31","DOI":"10.1145\/3394171.3413680"},{"unstructured":"Helminger, L., Djelouah, A., Gross, M., and Schroers, C. (2021, January 6). Lossy Image Compression with Normalizing Flows. Proceedings of the Neural Compression: From Information Theory to Applications\u2014Workshop @ ICLR 2021, Virtually.","key":"ref_32"},{"key":"ref_33","first-page":"12878","article-title":"Idempotent learned image compression with right-inverse","volume":"36","author":"Li","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"unstructured":"Xu, T., Zhu, Z., He, D., Li, Y., Guo, L., Wang, Y., Wang, Z., Qin, H., Wang, Y., and Liu, J. (2024). Idempotence and perceptual image compression. arXiv.","key":"ref_34"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TIP.2013.2293423","article-title":"Gradient magnitude similarity deviation: A highly efficient perceptual image quality index","volume":"23","author":"Xue","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2352\/ISSN.2470-1173.2016.16.HVEI-103","article-title":"Perceptual image quality assessment using a normalized Laplacian pyramid","volume":"2016","author":"Laparra","year":"2016","journal-title":"Electron. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016, January 11\u201314). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","key":"ref_38","DOI":"10.1007\/978-3-319-46475-6_43"},{"doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","key":"ref_39","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_40","first-page":"13","article-title":"An unsupervised information-theoretic perceptual quality metric","volume":"33","author":"Bhardwaj","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_41","first-page":"2567","article-title":"Image quality assessment: Unifying structure and texture similarity","volume":"44","author":"Ding","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"(2024, November 02). CLIC 2021: Workshop and Challenge on Learned Image Compression. Available online: https:\/\/clic.compression.cc\/2021\/tasks\/index.html.","key":"ref_42"},{"unstructured":"Zhu, H., Chen, B., Zhu, L., Wang, S., and Lin, W. (2022). DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator. arXiv.","key":"ref_43"},{"doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., and Bethge, M. (2015). A neural algorithm of artistic style. arXiv.","key":"ref_44","DOI":"10.1167\/16.12.326"},{"doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_45","DOI":"10.1109\/CVPR.2017.19"},{"doi-asserted-by":"crossref","unstructured":"Huang, C.H., and Wu, J.L. (2023, January 4\u20137). Image Data Hiding in Neural Compressed Latent Representations. Proceedings of the IEEE International Conference on Visual Communications and Image Processing (VCIP), Jeju, Republic of Korea.","key":"ref_46","DOI":"10.1109\/VCIP59821.2023.10402627"},{"unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013). Intriguing properties of neural networks. arXiv.","key":"ref_47"},{"unstructured":"Goodfellow, I.J., Shlens, J., and Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv.","key":"ref_48"},{"doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., and Bengio, S. (2018). Adversarial examples in the physical world. Artificial Intelligence Safety and Security, Chapman and Hall\/CRC.","key":"ref_49","DOI":"10.1201\/9781351251389-8"},{"unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv.","key":"ref_50"},{"doi-asserted-by":"crossref","unstructured":"Carlini, N., and Wagner, D. (2017, January 22\u201326). Towards evaluating the robustness of neural networks. Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","key":"ref_51","DOI":"10.1109\/SP.2017.49"},{"unstructured":"Zhu, T., Sun, H., Xiong, X., Zhu, X., Gong, Y., and Fan, Y. (2024). Attack and defense analysis of learned image compression. arXiv.","key":"ref_52"},{"doi-asserted-by":"crossref","unstructured":"Huang, C.H., and Wu, J.L. (2024). Joint Image Data Hiding and Rate-Distortion Optimization in Neural Compressed Latent Representations. MultiMedia Modeling, Springer.","key":"ref_53","DOI":"10.1007\/978-3-031-53305-1_8"},{"doi-asserted-by":"crossref","unstructured":"Zhu, J., Kaplan, R., Johnson, J., and Fei-Fei, L. (2018, January 8\u201314). Hidden: Hiding data with deep networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","key":"ref_54","DOI":"10.1007\/978-3-030-01267-0_40"},{"unstructured":"Shin, R., and Song, D. (2017, January 8). Jpeg-resistant adversarial images. Proceedings of the NIPS 2017 Workshop on Machine Learning and Computer Security, Long Beach, CA, USA.","key":"ref_55"},{"unstructured":"B\u00e9gaint, J., Racap\u00e9, F., Feltman, S., and Pushparaja, A. (2020). CompressAI: A PyTorch library and evaluation platform for end-to-end compression research. arXiv.","key":"ref_56"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"unstructured":"(2024, October 10). Kodak PhotoCD Dataset. Available online: http:\/\/r0k.us\/graphics\/kodak\/.","key":"ref_58"},{"doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","key":"ref_59","DOI":"10.1109\/CVPR.2019.00453"},{"doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","key":"ref_60","DOI":"10.1109\/CVPRW.2017.150"},{"unstructured":"Li, L., Bao, J., Yang, H., Chen, D., and Wen, F. (2019). Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv.","key":"ref_61"},{"unstructured":"(2024, September 28). Remaker Face Swap Online Free. Available online: https:\/\/remaker.ai\/face-swap-free\/.","key":"ref_62"},{"doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models. arXiv.","key":"ref_63","DOI":"10.1109\/CVPR52688.2022.01042"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:29:16Z","timestamp":1759919356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,15]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["bdcc9010014"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9010014","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,1,15]]}}}