Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger
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Yap-Peng Tan | Yufei Wang | Wenhan Yang | Shijian Lu | A. Kot | Yi Yu
[1] B. Wen,et al. Raw Image Reconstruction with Learned Compact Metadata , 2023, ArXiv.
[2] B. Wen,et al. ShadowFormer: Global Context Helps Image Shadow Removal , 2023, AAAI.
[3] Y. Liu,et al. M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System , 2023, ArXiv.
[4] Chen Kong,et al. Digital and Physical Face Attacks: Reviewing and One Step Further , 2022, APSIPA Transactions on Signal and Information Processing.
[5] H. Pfister,et al. ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal , 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Ruigang Liang,et al. Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain , 2022, ArXiv.
[7] Yap-Peng Tan,et al. Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Yong Xia,et al. FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Lap-Pui Chau,et al. Low-Light Image Enhancement with Normalizing Flow , 2021, AAAI.
[10] Yong Jiang,et al. Backdoor Learning: A Survey , 2020, IEEE transactions on neural networks and learning systems.
[11] Chen Kong,et al. Beyond the Pixel World: A Novel Acoustic-Based Face Anti-Spoofing System for Smartphones , 2022, IEEE Transactions on Information Forensics and Security.
[12] Hanghang Tong,et al. Backdoor Attack through Frequency Domain , 2021, ArXiv.
[13] Yap-Peng Tan,et al. Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions , 2021, NeurIPS Datasets and Benchmarks.
[14] Khoa D Doan,et al. LIRA: Learnable, Imperceptible and Robust Backdoor Attacks , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[15] George Kesidis,et al. A Backdoor Attack against 3D Point Cloud Classifiers , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[16] R. Jia,et al. Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[17] Joey Tianyi Zhou,et al. PointBA: Towards Backdoor Attacks in 3D Point Cloud , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] Shutao Xia,et al. Hidden Backdoor Attack against Semantic Segmentation Models , 2021, ArXiv.
[19] A. Tran,et al. WaNet - Imperceptible Warping-based Backdoor Attack , 2021, ICLR.
[20] Siwei Lyu,et al. Invisible Backdoor Attack with Sample-Specific Triggers , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] Zhan Ma,et al. End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling , 2019, IEEE Transactions on Image Processing.
[22] Minhui Xue,et al. Invisible Backdoor Attacks on Deep Neural Networks Via Steganography and Regularization , 2019, IEEE Transactions on Dependable and Secure Computing.
[23] Bernard Ghanem,et al. Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain , 2021, ArXiv.
[24] Anh Tran,et al. Input-Aware Dynamic Backdoor Attack , 2020, NeurIPS.
[25] Yunfei Liu,et al. Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks , 2020, ECCV.
[26] Fan Yang,et al. An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks , 2020, KDD.
[27] Michael Backes,et al. BadNL: Backdoor Attacks Against NLP Models , 2020, ArXiv.
[28] Wenhan Yang,et al. Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression , 2020, AAAI.
[29] Kilian Q. Weinberger,et al. TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks , 2020, ArXiv.
[30] Masaru Takeuchi,et al. Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Deliang Fan,et al. TBT: Targeted Neural Network Attack With Bit Trojan , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Walter J. Scheirer,et al. Backdooring Convolutional Neural Networks via Targeted Weight Perturbations , 2018, 2020 IEEE International Joint Conference on Biometrics (IJCB).
[33] Timo Aila,et al. A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Shawn D. Newsam,et al. Improving Semantic Segmentation via Video Propagation and Label Relaxation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Jooyoung Lee,et al. Context-adaptive Entropy Model for End-to-end Optimized Image Compression , 2018, ICLR.
[36] David Minnen,et al. Joint Autoregressive and Hierarchical Priors for Learned Image Compression , 2018, NeurIPS.
[37] Logan Engstrom,et al. Black-box Adversarial Attacks with Limited Queries and Information , 2018, ICML.
[38] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[39] David Minnen,et al. Variational image compression with a scale hyperprior , 2018, ICLR.
[40] W. Freeman,et al. Video Enhancement with Task-Oriented Flow , 2017, International Journal of Computer Vision.
[41] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[42] Dawn Xiaodong Song,et al. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning , 2017, ArXiv.
[43] Brendan Dolan-Gavitt,et al. BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain , 2017, ArXiv.
[44] Percy Liang,et al. Certified Defenses for Data Poisoning Attacks , 2017, NIPS.
[45] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[46] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] David Minnen,et al. Variable Rate Image Compression with Recurrent Neural Networks , 2015, ICLR.
[49] Valero Laparra,et al. Density Modeling of Images using a Generalized Normalization Transformation , 2015, ICLR.
[50] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[51] Gary J. Sullivan,et al. Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.
[52] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[53] Daniel T. Lee. JPEG 2000: Retrospective and New Developments , 2005, Proceedings of the IEEE.
[54] Gregory K. Wallace,et al. The JPEG still picture compression standard , 1991, CACM.