When constructing a deep learning model for recognizing violence inside a vehicle, it is crucial to consider several aspects. One aspect is the computational limitations, and the other is the deep learning model architecture chosen. Nevertheless, to choose the best deep learning model, it is necessary to test and evaluate the model against adversarial attacks. This paper presented three different architecture models for violence recognition inside a vehicle. These model architectures were evaluated based on adversarial attacks and interpretability methods. An analysis of the model’s convergence was conducted, followed by adversarial robustness for each model and a sanity-check based on interpretability analysis. It compared a standard evaluation for training and testing data samples with the adversarial attacks techniques. These two levels of analysis are essential to verify model weakness and sensibility regarding the complete video and in a frame-by-frame way. ; This work is funded by “FCT—Fundação para a Ciência e Tecnologia” within the R&D Units Project Scope: UIDB/00319/2020. The employment contract of Dalila Durães is supported by CCDR-N Project: NORTE-01-0145-FEDER-000086
Weakness evaluation on in-vehicle violence detection: an assessment of X3D, C2D and I3D against FGSM and PGD
2022-03-01
doi:10.3390/electronics11060852
Article (Journal)
Electronic Resource
English
DDC: | 629 |
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