An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved Convolutional Neural Network (ICNN) model optimized using a Gradient-based Optimization (GBO)(ICNN-GBO) algorithm. In the ICNN-GBO model, we are injecting the triggers via a steganography and regularization technique. We are generating triggers using a single-pixel, irregular shape, and different sizes. The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate, stealthiness, pollution index, anomaly index, entropy index, and functionality. When the CNN-GBO model is trained with the poisoned dataset, it will map the malicious code to the target label. The proposed scheme's effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 and MSCELEB 1M dataset. The results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.