{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T13:47:18Z","timestamp":1775051238200,"version":"3.50.1"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,28]]},"DOI":"10.1109\/pst62714.2024.10788056","type":"proceedings-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T19:15:09Z","timestamp":1734376509000},"page":"1-11","source":"Crossref","is-referenced-by-count":1,"title":["DevilDiffusion: Embedding Hidden Noise Backdoors into Diffusion Models"],"prefix":"10.1109","author":[{"given":"William","family":"Aiken","sequence":"first","affiliation":[{"name":"School of EECS, University of Ottawa,Ottawa,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paula","family":"Branco","sequence":"additional","affiliation":[{"name":"School of EECS, University of Ottawa,Ottawa,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guy-Vincent","family":"Jourdan","sequence":"additional","affiliation":[{"name":"School of EECS, University of Ottawa,Ottawa,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"},{"key":"ref2","author":"Azizi","year":"2023","journal-title":"Syn-thetic data from diffusion models improves ImageN et classification"},{"key":"ref3","first-page":"4015","article-title":"How to backdoor diffu-sion models?","volume-title":"Proceedings of the IEEEICVF Conference on Computer Vision and Pattern Recognition","author":"Chou","year":"2023"},{"key":"ref4","article-title":"VillanDiffusion: A unified backdoor attack framework for dif-fusion models","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Chou","year":"2024"},{"key":"ref5","volume-title":"After AI-generated porn report, Washington Lottery pulls down interactive web app","author":"Orland","year":"2024"},{"key":"ref6","volume-title":"Meta\u2019s AI stickers are here and already causing contro-versy","author":"Goldman","year":"2023"},{"key":"ref7","volume-title":"Microsoft engineer warns company\u2019s ai tool creates violent, sexual images, ignores copyrights","author":"Field","year":"2024"},{"key":"ref8","volume-title":"Text-to-image AI models can be tricked into generating disturbing images","author":"Williams","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3616679"},{"key":"ref10","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Advances in neural information processing systems"},{"issue":"47","key":"ref11","first-page":"1","article-title":"Cascaded diffusion models for high fidelity image generation","volume":"23","author":"Ho","year":"2022","journal-title":"Journal of Machine Learning Research"},{"key":"ref12","author":"Ho","year":"2022","journal-title":"Classifier-free diffusion guidance"},{"key":"ref13","first-page":"3","volume":"1","author":"Ramesh","year":"2022","journal-title":"Hierarchical text -conditional image generation with clip latents"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref15","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume":"35","author":"Saharia","year":"2022","journal-title":"Advances in neural information processing systems"},{"key":"ref16","first-page":"8162","article-title":"Improved denoising diffusion probabilis-tic models","volume-title":"International conference on machine learning","author":"Nichol","year":"2021"},{"key":"ref17","article-title":"Denoising diffusion implicit models","volume-title":"International Conference on Learning Representations","author":"Song","year":"2021"},{"key":"ref18","first-page":"26565","article-title":"Elucidating the de-sign space of diffusion-based generative models","volume-title":"Advances in Neu-ral Information Processing Systems","volume":"35","author":"Karras","year":"2022"},{"key":"ref19","first-page":"5775","article-title":"DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Lu","year":"2022"},{"key":"ref20","year":"2022","journal-title":"DPM-Solver++: Fast solver for guided sampling of diffusion probabilistic models"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3361474"},{"key":"ref22","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume":"30","author":"Heusel","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2909068"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3196494.3196550"},{"key":"ref25","first-page":"1615","article-title":"Turning your weakness into a strength: Watermarking deep neural networks by back-dooring","volume-title":"27th USENIX Security Symposium (USENIX Security 18)","author":"Adi","year":"2018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23291"},{"key":"ref27","author":"Turner","year":"2019","journal-title":"Label-consistent backdoor at-tacks"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00393"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00423"},{"key":"ref30","volume-title":"Learnable invisible backdoor for diffusion models","author":"Li","year":"2024"},{"key":"ref31","article-title":"Detecting backdoor attacks on deep neural networks by activation clustering","volume-title":"CEUR Workshop Proceedings AAAI Workshop on Artificial Intelligence Safety","author":"Chen","year":"2018"},{"key":"ref32","article-title":"Spectral signatures in back-door attacks","volume-title":"Advances in neural information processing systems","volume":"31","author":"Tran","year":"2018"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00025"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28958"},{"key":"ref36","author":"Sui","year":"2024","journal-title":"DisDet: Exploring detectability of backdoor attack on dif-fusion models"},{"key":"ref37","first-page":"16913","article-title":"Adversarial neuron pruning purifies backdoored deep models","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Wu","year":"2021"},{"key":"ref38","author":"Kingma","year":"2013","journal-title":"Auto-encoding variational bayes"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","article-title":"Attention is all you need","volume-title":"Advances in neural information processing systems","volume":"30","author":"Vaswani","year":"2017"},{"key":"ref42","article-title":"Learning multiple layers of fea-tures from tiny images","volume-title":"Technical Report","author":"Krizhevsky","year":"2009"},{"key":"ref43","article-title":"CAl neural API","author":"Schuler","year":"2021","journal-title":"10.5281\/zen-"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.02.016"},{"key":"ref45","first-page":"12","article-title":"The KTH-TIPS2 database","volume-title":"Computational Vi-sion and Active Perception Laboratory, Stockholm, Sweden","volume":"11","author":"Mallikarjuna","year":"2006"},{"key":"ref46","article-title":"The KTH-TIPS database","volume-title":"Computational Vision and Active Perception Labora-tory, Stockholm, Sweden","author":"Fritz","year":"2004"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103329"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00611"},{"key":"ref49","article-title":"Progressive growing of GAN s for improved quality, stability, and variation","volume-title":"International Conference on Learning Representations","author":"Karras","year":"2018"}],"event":{"name":"2024 21st Annual International Conference on Privacy, Security and Trust (PST)","location":"Sydney, Australia","start":{"date-parts":[[2024,8,28]]},"end":{"date-parts":[[2024,8,30]]}},"container-title":["2024 21st Annual International Conference on Privacy, Security and Trust (PST)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10788036\/10788037\/10788056.pdf?arnumber=10788056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T05:51:37Z","timestamp":1734414697000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10788056\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/pst62714.2024.10788056","relation":{},"subject":[],"published":{"date-parts":[[2024,8,28]]}}}