Redefining Anti-Forensics in Digital Image Operation: Exploring the Power of Dual-Domain Generative Adversarial Networks

Millimeter-wave (mmWave) radar may be used in a variety of imaginative ways to monitor and observe several things at the same time. However, problems with the radar itself are possible. It is challenging to improve mmWave radar detection while maintaining reliable data categorization. Building a reliable model that is consistent with tracking and sensing goals is a significant barrier to realising the full potential of mmWave sensing. Because radar frames are associated with major events, manually annotating mmWave data takes a lot of time and frequently requires topic expertise. This article lays the groundwork for training mmWave radar by utilising a camera as a labelling and monitoring tool. During the research phase, a preliminary drawing of the framework was constructed. The proposed strategy is evaluated in contrast to other methods that strive to achieve the same goals. Experiments in a variety of real-world settings have verified the conceptual framework. The mmWave technology is used in the multi-object tracking system that arises from the stated architectural design. This technology differs from others in that it can discriminate between sprinting, tripping, and simply strolling. The results of the experiment show that guided tagging from a camera may be utilised to train a radar model consistently. This trained model consistently demonstrates great classification accuracy over a wide range of instances, not just the ones it was trained on. This report lays the groundwork for further research into hybrid monitoring setups. It specifically supports the construction of mmWave classification models by tackling labelling and training issues.