Construction and Evaluation of Benchmark Da-Tasets For Adversarial Ai Research

Standard offensive AI datasets need to be made and tested to improve the performance of AI models. Using adversarial diversity-driven dataset building (ADDC), this work shows new ways to create datasets and ways to measure safety and power. The DCA fakes several aggressive events to make sure that the benchmark dataset has a variety of strikes and pauses within it. When the REA is run, it shows how well AI models can handle certain threats. A vulnerability avoidance algorithm (VMA) finds holes in an AI model and stops them from being used. We compare the suggested plan to ADS, APDA, RMAAI, AVP, CABG, and AASF based on several factors that affect results. The suggested method both lowers the number of mistakes and raises the variety, which improves the dataset and makes sure that a full review is done. Artificial intelligence models that use the suggested method are less likely to be hacked online. First, well-known datasets for study into hostile artificial intelligence are put together and studied.