Abstract

The requirement for strong defenses against complex adversarial assaults is increasing fast in the constantly evolving AI ecosystem. Considering this need, we put up the Explain Defend Net architecture, a unique adversarial defensive mechanism. This framework utilizes state-of-the-art methods to improve the robustness, openness, and flexibility of models. To protect the model from external interference, the Robust Feature Recalibrator (RFR) selectively adjusts the calibration of input features. The Explain Intercept Layer (EIL) offers transparency by offering interpretable insights into the decision-making process, enhancing human comprehension. The model can adapt to new forms of adversarial attack because of the dynamic adaptability guaranteed by Adaptive Reinforce Guard (ARG). With its comprehensive defensive strategy, Explain Defend Net is designed to outperform more conventional approaches. The suggested framework is put through rigorous testing, and the results indicate that it outperforms six established approaches in a wide range of categories. The findings show that Explain Defend Net regularly outperforms conventional techniques, proving its efficacy in protecting AI systems from malicious actors. Explain Defend Net is state-of-the-art in the field of adversarial defense because of its novel mix of recalibration, interpretability, and adaptive reinforcement.