CNN Classifier's Robustness Enhancement when Preserving Privacy
Laws on privacy preservation challenges supervised learning algorithms in industrial applications and could be an obstacle for the artificial intelligence solutions. In the literature, this issue is never discussed for the algorithm’s design. Indeed, algorithms do not behave the same when the input is modified to protect privacy. Particularly, the unmodified data samples predicts with low confidences show high vulnerability to decision changes. To overcome this challenge, we propose a novel solution that enhances classifier’s robustness by particularly addressing the vulnerable samples. It consists of a novel formulation of the learning objective by hybridizing similarity learning, decision margin and intra-class distance. Experimental results and evaluation on a challenging vehicle image dataset exhibit the high effectiveness and potentials of our method for the privacy preserving classification problems.
laws on privacy preservation challenges, the artificial intelligence solutions, hybridizing similarity learning, Privacy, Vehicle Classification, CNN, conference abstracts
Abul Hasnat. CNN Classifier's Robustness Enhancement when Preserving Privacy / Hasnat Abul, Nadiya Shvai, Amir Nakib // IEEE International Conference on Image Processing (ICIP). - 2021. - P. 3887-3891. - https://doi.org/10.1109/ICIP42928.2021.9506188