Towards evaluating robustness of violence detection in videos using cross-domain transferability
Tài liệu tham khảo
Krizhevsky, 2017, Imagenet classification with deep convolutional neural networks, Commun ACM, 60, 84, 10.1145/3065386
Eykholt, 2018, Robust physical-world attacks on deep learning visual classification, 1625
Long, 2015, Fully convolutional networks for semantic segmentation, 3431
Ren, 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, Adv Neural Inf Process Syst, 28
Sharif, 2016, Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition, 1528
Wei, 2020, Heuristic black-box adversarial attacks on video recognition models, 12338
Sultani, 2018, Real-world anomaly detection in surveillance videos, 6479
Khan, 2019, Cover the violence: A novel Deep-Learning-Based approach towards violence-detection in movies, Appl Sci, 9, 4963, 10.3390/app9224963
Mabrouk, 2018, Abnormal behavior recognition for intelligent video surveillance systems: A review, Expert Syst Appl, 91, 480, 10.1016/j.eswa.2017.09.029
Ye, 2020, A video-based DT–SVM school violence detecting algorithm, Sensors, 20, 2018, 10.3390/s20072018
Zhang, 2016, A new method for violence detection in surveillance scenes, Multimedia Tools Appl, 75, 7327, 10.1007/s11042-015-2648-8
Ding, 2014, Violence detection in video by using 3D convolutional neural networks, 551
Ullah, 2019, Violence detection using spatiotemporal features with 3D convolutional neural network, Sensors, 19, 2472, 10.3390/s19112472
Sudhakaran, 2017, Learning to detect violent videos using convolutional long short-term memory, 1
Chen, 2009
Freire-Obregón, 2022, Inflated 3D ConvNet context analysis for violence detection, Mach Vis Appl, 33, 1, 10.1007/s00138-021-01264-9
Wojke, 2017, Simple online and realtime tracking with a deep association metric, 3645
Zhang, 2019, Deeper and wider siamese networks for real-time visual tracking, 4591
Abdali, 2019, Robust real-time violence detection in video using cnn and lstm, 104
latest in Machine Learning T. Papers with code - hockey fight detection dataset benchmark (video classification). dataset.
Wei, 2019, Sparse adversarial perturbations for videos, 8973
Inkawhich, 2018
Jiang, 2019, Black-box adversarial attacks on video recognition models, 864
Wei, 2022, Cross-modal transferable adversarial attacks from images to videos, 15064
He, 2016, Deep residual learning for image recognition, 770
Liu, 2020, Hyperbolic visual embedding learning for zero-shot recognition, 9273
Chen, 2020, Zero-shot ingredient recognition by multi-relational graph convolutional network, 10542
Jiao, 2021, Two-stage visual cues enhancement network for referring image segmentation, 1331
Wu, 2020, A dynamic frame selection framework for fast video recognition, IEEE Trans Pattern Anal Mach Intell, 44, 1699, 10.1109/TPAMI.2020.3029425
Song, 2021, Spatial-temporal graphs for cross-modal text2video retrieval, IEEE Trans Multimed
Tran, 2015, Learning spatiotemporal features with 3d convolutional networks, 4489
Shi, 2015, Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Adv Neural Inf Process Syst, 28
LeCun, 1998, Gradient-based learning applied to document recognition, Proc IEEE, 86, 2278, 10.1109/5.726791
Hochreiter, 1997, Long short-term memory, Neural Comput, 9, 1735, 10.1162/neco.1997.9.8.1735
Carreira, 2017, Quo vadis, action recognition? a new model and the kinetics dataset, 6299
Yao, 2021, Motion direction inconsistency-based fight detection for multiview surveillance videos, Wirel Commun Mob Comput, 2021, 10.1155/2021/9965781
Febin, 2020, Violence detection in videos for an intelligent surveillance system using MoBSIFT and movement filtering algorithm, Pattern Anal Appl, 23, 611, 10.1007/s10044-019-00821-3
Bermejo Nievas, 2011, Violence detection in video using computer vision techniques, 332
Deepak, 2020, Autocorrelation of gradients based violence detection in surveillance videos, ICT Express, 6, 155, 10.1016/j.icte.2020.04.014
Sarcar, 2021, Detecting violent arm movements using CNN-LSTM, 1
Fenil, 2019, Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM, Comput Netw, 151, 191, 10.1016/j.comnet.2019.01.028
Goodfellow, 2014
Szegedy, 2013
Kurakin, 2016
Moosavi-Dezfooli, 2016, Deepfool: a simple and accurate method to fool deep neural networks, 2574
Carlini, 2017, Towards evaluating the robustness of neural networks, 39
Guo, 2021
Yuan, 2019, Adversarial examples: Attacks and defenses for deep learning, IEEE Trans Neural Netw Learn Syst, 30, 2805, 10.1109/TNNLS.2018.2886017
Xiao, 2018
Goodfellow, 2020, Generative adversarial networks, Commun ACM, 63, 139, 10.1145/3422622
Rey-de Castro, 2018
Zajac, 2019, Adversarial framing for image and video classification, 10077
Pony, 2021, Over-the-air adversarial flickering attacks against video recognition networks, 515
Chen, 2021, Appending adversarial frames for universal video attack, 3199
Xu, 2022, C-fdrl: Context-aware privacy-preserving offloading through federated deep reinforcement learning in cloud-enabled IoT, IEEE Trans Ind Inf, 19, 1155, 10.1109/TII.2022.3149335
Xu, 2020, A blockchain-enabled deduplicatable data auditing mechanism for network storage services, IEEE Trans Emerg Top Comput, 9, 1421, 10.1109/TETC.2020.3005610
Xu, 2020, Blockchain-enabled accountability mechanism against information leakage in vertical industry services, IEEE Trans Netw Sci Eng, 8, 1202, 10.1109/TNSE.2020.2976697
Tian, 2021, Weakly-supervised video anomaly detection with robust temporal feature magnitude learning, 4975
Kurakin, 2018, Adversarial examples in the physical world, 99
Dong, 2018, Boosting adversarial attacks with momentum, 9185
Wu, 2020
Dong, 2019, Evading defenses to transferable adversarial examples by translation-invariant attacks, 4312
Lin, 2019
Wu, 2020, Boosting the transferability of adversarial samples via attention, 1161
Wei, 2022, Boosting the transferability of video adversarial examples via temporal translation, 2659
Rauber, 2020, Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax, J Open Source Softw, 5, 2607, 10.21105/joss.02607