Interactive analysis of CNN robustness

article Interactive analysis of CNN robustness published yes Stefan Sietzen author Mathias Lechner author 3DC22916-F248-11E8-B48F-1D18A9856A87 Judy Borowski author Ramin Hasani author Manuela Waldner author ToHe department Formal methods for the design and analysis of complex systems project While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web-based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine-tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users’ insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models. Wiley2021 eng Computer Graphics Forum 0167-7055 1467-8659 2110.07667 00072295200002410.1111/cgf.14418 407253-264 Sietzen S, Lechner M, Borowski J, Hasani R, Waldner M. 2021. Interactive analysis of CNN robustness. Computer Graphics Forum. 40(7), 253–264. Sietzen, Stefan, Mathias Lechner, Judy Borowski, Ramin Hasani, and Manuela Waldner. “Interactive Analysis of CNN Robustness.” Computer Graphics Forum. Wiley, 2021. <a href="https://doi.org/10.1111/cgf.14418">https://doi.org/10.1111/cgf.14418</a>. Sietzen S, Lechner M, Borowski J, Hasani R, Waldner M. Interactive analysis of CNN robustness. Computer Graphics Forum. 2021;40(7):253-264. doi:<a href="https://doi.org/10.1111/cgf.14418">10.1111/cgf.14418</a> Sietzen, S., Lechner, M., Borowski, J., Hasani, R., & Waldner, M. (2021). Interactive analysis of CNN robustness. Computer Graphics Forum. Wiley. <a href="https://doi.org/10.1111/cgf.14418">https://doi.org/10.1111/cgf.14418</a> S. Sietzen, M. Lechner, J. Borowski, R. Hasani, M. Waldner, Computer Graphics Forum 40 (2021) 253–264. S. Sietzen, M. Lechner, J. Borowski, R. Hasani, and M. Waldner, “Interactive analysis of CNN robustness,” Computer Graphics Forum, vol. 40, no. 7. Wiley, pp. 253–264, 2021. Sietzen, Stefan, et al. “Interactive Analysis of CNN Robustness.” Computer Graphics Forum, vol. 40, no. 7, Wiley, 2021, pp. 253–64, doi:<a href="https://doi.org/10.1111/cgf.14418">10.1111/cgf.14418</a>. 104042021-12-05T23:01:40Z2025-04-15T06:25:55Z