Published Paper Details:

In the vast landscape of machine learning, the emergence of adversarial threats has cast a shadow over the reliability and security of deployed models. With the proliferation of sophisticated attacks aimed at undermining the integrity of machine learning systems, the imperative for robust defenses has never been more pronounced. Against this backdrop, this paper embarks on a comprehensive journey through the adversarial landscape, surveying the myriad threats and safeguards that define the contemporary discourse in machine learning security. Under the banner of "Navigating the Adversarial Landscape," this survey endeavors to shed light on the intricate interplay between adversarial attacks and defensive strategies. By analyzing the life structures of ill- disposed dangers and examining the viability of existing protections, this try looks to outfit per users with a nuanced comprehension of the difficulties and open doors intrinsic in defending AI frameworks. As we embark on this expedition, we delve into the nuanced nuances of adversarial attacks, encompassing a spectrum of techniques ranging from subtle perturbations to outright manipulations. From white-box to black-box attacks, and from transfer to physical assaults, we unravel the diverse tactics employed by adversaries to subvert machine learning systems. However, amidst the looming specter of adversarial threats, glimmers of hope emerge through the pursuit of robust defense mechanisms. Through adversarial training, robust optimization, and certified defenses, among other strategies, researchers endeavor to fortify machine learning models against adversarial incursions. Ultimately, the quest to navigate the adversarial landscape represents not only a technical challenge but also a moral imperative in safeguarding the integrity and trustworthiness of machine learning systems.