Defusing Dirty Label Backdoor Attack With Differentially Private Data Through Feature Vectorized AlexNet for Face Gender Classification

In any commercial applications, gender is an important demographic factor that can be utilized to understand the future of retail and the nature of shopping. Nevertheless, a variety of variances in viewing perspectives, facial emotions, extreme instances, background, resolution variations, and face image appearance make gender classification a constant challenging task. This motivates us to propose Canny Edge Feature Vectorized Dense Layered AlexNet (CFVD- AlexNet) inspired by AlexNet which classifies the gender of the face images with high accuracy. The main contribution of this research is three-fold. The initial contribution is through feature extraction using unsupervised learning which is based on the Canny Edge Feature (CEF) extraction technique. The second contribution deals with fine-tuning the AlexNet to design CFVD- AlexNet. The final contribution towards the security countermeasure which overcomes the Dirty Label Backdoor attack method by proposing the CFVD Differentially Private Data Augmentations AlexNet (CFVD DPInstaHide AlexNet). The Face Gender Classification Dataset with 5200 face images is subjected to CEF preprocessing that forms significant Canny Edge Feature Vectorized (CFV) face images. The CFV face images are fitted with the existing CNN models to choose the best CNN model. Experiment results portray that AlexNet and DenseNet offer gender classification with an accuracy above 85% compared to other CNN models. Now, AlexNet has been chosen to add an extra three-layered Dense block after the Fully Connected Layer (FCL) ending with the CFVD- AlexNet model. The implementation results reveal that the proposed CFVD-AlexNet classifies the gender with a high accuracy of 99.32% when compared to the existing models. The Novelty of this research relies on suppressing the Dirty Label Backdoor (DLB) attack by the proposed CFVD DPInstaHide AlexNet model in which the set of security countermeasure of the model are also validated for the successful implementation.