{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T17:22:53Z","timestamp":1782926573195,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T00:00:00Z","timestamp":1541721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Today, the main two security issues for deep learning are data poisoning and adversarial examples. Data poisoning consists of perverting a learning system by manipulating a small subset of the training data, while adversarial examples entail bypassing the system at testing time with low-amplitude manipulation of the testing sample. Unfortunately, data poisoning that is invisible to human eyes can be generated by adding adversarial noise to the training data. The main contribution of this paper includes a successful implementation of such invisible data poisoning using image classification datasets for a deep learning pipeline. This implementation leads to significant classification accuracy gaps.<\/jats:p>","DOI":"10.3390\/make1010011","type":"journal-article","created":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T03:27:31Z","timestamp":1542079651000},"page":"192-204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7333-2765","authenticated-orcid":false,"given":"Adrien","family":"CHAN-HON-TONG","sequence":"first","affiliation":[{"name":"ONERA, Universit\u00e9 de Paris Saclay, F-91123 Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,9]]},"reference":[{"key":"ref_1","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014, January 24\u201327). Deepface: Closing the Gap to Human-Level Performance in Face Verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans. Med. Imagingg"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alsulami, B., Dauber, E., Harang, R., Mancoridis, S., and Greenstadt, R. (2017). Source Code Authorship Attribution Using Long Short-Term Memory Based Networks. European Symposium on Research in Computer Security, Springer.","DOI":"10.1007\/978-3-319-66402-6_6"},{"key":"ref_7","unstructured":"Javaid, A., Niyaz, Q., Sun, W., and Alam, M. A Deep Learning Approach for Network Intrusion Detection System. Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Moosavi Dezfooli, S.M., Fawzi, A., Fawzi, O., and Frossard, P. (2017, January 21\u201326). Universal Adversarial Perturbations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.17"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., and Yuille, A. (2017, January 22\u201329). Adversarial Examples for Semantic Segmentation and Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.153"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., and Swami, A. (2016, January 21\u201324). The Limitations of Deep Learning in Adversarial Settings. Proceedings of the 2016 IEEE European Symposium on Security and Privacy (EuroS&P), Saarbrucken, Germany.","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref_11","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., and Fergus, R. (arXiv, 2013). Intriguing Properties of Neural Networks, arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Grosse, K., Papernot, N., Manoharan, P., Backes, M., and McDaniel, P. (2017). Adversarial Examples for Malware Detection. European Symposium on Research in Computer Security, Springer.","DOI":"10.1007\/978-3-319-66399-9_4"},{"key":"ref_13","unstructured":"Cisse, M.M., Adi, Y., Neverova, N., and Keshet, J. (2017). Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Narodytska, N., and Kasiviswanathan, S. (2017, January 21\u201326). Simple Black-Box Adversarial Attacks on Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.172"},{"key":"ref_15","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (arXiv, 2016). Understanding Deep Learning Requires Rethinking Generalization, arXiv."},{"key":"ref_16","unstructured":"Kurakin, A., Goodfellow, I.J., and Bengio, S. (arXiv, 2016). Adversarial Examples in the Physical World, arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/COMST.2018.2800740","article-title":"Detecting and Preventing Cyber Insider Threats: A Survey","volume":"20","author":"Liu","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_18","unstructured":"Mu\u00f1oz-Gonz\u00e1lez, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E.C., and Roli, F. Towards Poisoning of Deep Learning Algorithms with Back-Gradient Optimization. Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security."},{"key":"ref_19","unstructured":"Krizhevsky, A., and Hinton, G.E. (2011, January 27\u201329). Using very Deep Autoencoders for Content-Based Image Retrieval. Proceedings of the 19th European Symposium on Artificial Neural Networks, Bruges, Belgium."},{"key":"ref_20","unstructured":"Vapnik, V.N., and Vapnik, V. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_21","unstructured":"LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., and Jackel, L.D. (1990). Handwritten Digit Recognition with a Back-Propagation Network. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fischler, M.A., and Bolles, R.C. (1987). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Readings in Computer Vision, Elsevier.","DOI":"10.1016\/B978-0-08-051581-6.50070-2"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, J., Wang, Y., Zhou, W., Xiang, Y., and Vel., O.D. (2018). A Data-driven Attack Against Support Vectors of SVM. Proceedings of the 2018 on Asia Conference on Computer and Communications Security, ACM. ASIACCS \u201918.","DOI":"10.1145\/3196494.3196539"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_26","first-page":"1871","article-title":"LIBLINEAR: A Library for Large Linear Classification","volume":"9","author":"Fan","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:28:58Z","timestamp":1760196538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,9]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["make1010011"],"URL":"https:\/\/doi.org\/10.3390\/make1010011","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,9]]}}}