1. Agarwal, A., Singh, R., Vatsa, M., Ratha, N.K.: Image transformation based defense against adversarial perturbation on deep learning models. IEEE Trans. Dependable Secure Comput. 15(5), 2106–2121 (2021)
2. Akhtar, N., Liu, J., Mian, A.: Defense against universal adversarial perturbations. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3389–3398 (2018)
3. Anscombe, F.J.: Fixed-sample-size analysis of sequential observations. Biometrics 10(1), 89–100 (1954)
4. Athalye, A., Carlini, N., Wagner, D.A.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: Proceedings of the 35th International Conference on Machine Learning, pp. 274–283 (2018)
5. Avriel, M., Wilde, D.J.: Optimally proof for the symmetric Fibonacci search technique. Fibonacci Q. J. (1966)