{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T17:58:53Z","timestamp":1730311133908,"version":"3.28.0"},"reference-count":31,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,5]]},"DOI":"10.1117\/12.2623585","type":"proceedings-article","created":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:56:59Z","timestamp":1646441819000},"page":"48","source":"Crossref","is-referenced-by-count":3,"title":["Integrating single-shot Fast Gradient Sign Method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers"],"prefix":"10.1117","author":[{"given":"Muhammad","family":"Hassan","sequence":"first","affiliation":[]},{"given":"Shahzad","family":"Younis","sequence":"additional","affiliation":[]},{"given":"Ahmed","family":"Rasheed","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[]}],"member":"189","reference":[{"key":"c1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"journal-title":"arXiv preprint arXiv:1604.07316","article-title":"End to end learning for self-driving cars","year":"2016","author":"Bojarski","key":"c2"},{"key":"c3","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever","year":"2014","journal-title":"In Advances in neural information processing systems"},{"journal-title":"arXiv preprint arXiv:1312.6199","article-title":"Intriguing properties of neural networks","year":"2013","author":"Szegedy","key":"c4"},{"journal-title":"IEEE Transactions on Dependable and Secure Computing","article-title":"Detecting adversarial image examples in deep neural networks with adaptive noise reduction","year":"2018","author":"Liang","key":"c5"},{"journal-title":"arXiv preprint arXiv:1703.00410","article-title":"Detecting adversarial samples from artifacts","year":"2017","author":"Feinman","key":"c6"},{"journal-title":"arXiv preprint arXiv:1702.04267","article-title":"On detecting adversarial perturbations","year":"2017","author":"Metzen","key":"c7"},{"journal-title":"arXiv preprint arXiv:1702.06280","article-title":"On the (statistical) detection of adversarial examples","year":"2017","author":"Grosse","key":"c8"},{"journal-title":"arXiv preprint arXiv:1704.01155","article-title":"Feature squeezing: Detecting adversarial examples in deep neural networks","year":"2017","author":"Xu","key":"c9"},{"key":"c10","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/1128817.1128824","article-title":"Can machine learning be secure?","volume-title":"Proceedings of the 2006 ACM Symposium on Information, computer and communications security","author":"Barreno","year":"2006"},{"key":"c11","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/IOLTS.2019.8854425","article-title":"Trisec: training data-unaware imperceptible security attacks on deep neural networks","volume-title":"2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS)","author":"Khalid","year":"2019"},{"journal-title":"arXiv preprint arXiv:1712.04248","article-title":"Decision-based adversarial attacks: Reliable attacks against black-box machine learning models","year":"2017","author":"Brendel","key":"c12"},{"journal-title":"arXiv preprint arXiv:1901.10258","article-title":"Red-attack: Resource efficient decision based attack for machine learning","year":"2019","author":"Khalid","key":"c13"},{"key":"c14","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.4235"},{"journal-title":"arXiv preprint arXiv:1611.01236","article-title":"Adversarial machine learning at scale","year":"2016","author":"Kurakin","key":"c15"},{"key":"c16","first-page":"1625","article-title":"Robust physical-world attacks on deep learning visual classification","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Eykholt","year":"2018"},{"key":"c17","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"journal-title":"arXiv preprint arXiv:1412.6572","article-title":"Explaining and harnessing adversarial examples","year":"2014","author":"Goodfellow","key":"c18"},{"journal-title":"arXiv preprint arXiv:1705.07204","article-title":"Ensemble adversarial training: Attacks and defenses","year":"2017","author":"Tram\u00e8r","key":"c19"},{"key":"c20","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.76"},{"key":"c21","first-page":"1","article-title":"Going deeper with convolutions","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Szegedy","year":"2015"},{"article-title":"Adversarial examples in the physical world","volume-title":"arXiv preprint arXiv:1607.02533","year":"2016","author":"Kurakin","key":"c22"},{"issue":"3","key":"c23","first-page":"275","article-title":"A dwt based approach for image steganography","volume":"4","author":"Chen","year":"2006","journal-title":"International Journal of Applied Science and Engineering"},{"key":"c24","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/978-3-642-30567-2_18","article-title":"Digital image steganography: An fft approach","volume-title":"International Conference on Networked Digital Technologies","author":"Rabie","year":"2012"},{"issue":"8","key":"c25","first-page":"1008","article-title":"Digital image watermarking using fractional fourier transform with different attacks","volume":"3","author":"Tiwari","year":"2014","journal-title":"International Journal of Scientific Engineering and Technology"},{"journal-title":"arXiv preprint arXiv:1702.02284","article-title":"Adversarial attacks on neural network policies","year":"2017","author":"Huang","key":"c26"},{"key":"c27","unstructured":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. Imagenet lsvrc 2012 validation set (object detection)."},{"key":"c28","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Szegedy","year":"2016"},{"journal-title":"arXiv preprint arXiv:1404.5997","article-title":"One weird trick for parallelizing convolutional neural networks","year":"2014","author":"Krizhevsky","key":"c29"},{"key":"c30","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"He","year":"2016"},{"article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"arXiv preprint arXiv:1409.1556","year":"2014","author":"Simonyan","key":"c31"}],"event":{"name":"Fourteenth International Conference on Machine Vision (ICMV 2021)","start":{"date-parts":[[2021,11,8]]},"location":"Rome, Italy","end":{"date-parts":[[2021,11,12]]}},"container-title":["Fourteenth International Conference on Machine Vision (ICMV 2021)"],"original-title":[],"deposited":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T22:32:38Z","timestamp":1655850758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12084\/2623585\/Integrating-single-shot-Fast-Gradient-Sign-Method-FGSM-with-classical\/10.1117\/12.2623585.full"}},"subtitle":[],"editor":[{"given":"Wolfgang","family":"Osten","sequence":"additional","affiliation":[]},{"given":"Dmitry","family":"Nikolaev","sequence":"additional","affiliation":[]},{"given":"Jianhong","family":"Zhou","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,3,5]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1117\/12.2623585","relation":{},"subject":[],"published":{"date-parts":[[2022,3,5]]}}}