Advancements in Defense Mechanisms against Adversarial Attacks in Computer Vision

More and more businesses are using computer vision systems, which has made it clear that they need strong defenses against threats from other companies. These attacks, which change raw data in small ways, can lead to big mistakes in classification, which makes AI-driven systems less reliable. There have been recent improvements in defense mechanisms for computer vision models against risks from other computers. This study paper looks at both old and new methods. The outcome documented in this study paper shows that different protection strategies work. Different levels of defense are provided by traditional techniques such as adversarial training, input preparation, and defensive distilling. Of these, adversarial training is the most effective against known threats. A lot of the time, these methods have trouble with new or complex hostile changes. More advanced methods, like verified stability and feature squeezing, work better, especially against more complicated threats. The most accurate defenses are hybrids that use antagonistic training along with feature manipulation or GAN-based cleaning. These methods offer a fuller defense. In the world of computer vision, the fight between attackers and defenders is still going on, and new attack methods are always coming out. New improvements in security systems have made computer vision models more durable, but there is still a lot of work to be done. In the future, hybrid and adaptable barriers could be very useful because they offer more flexible and strong security. To keep AI systems safe and reliable, these defenses must keep getting better. This is especially important for important uses like self-driving cars, healthcare, and spying.