The Dark Side of AI: How Adversarial Noise Can Fool Neural Networks
Neural networks, the backbone of modern artificial intelligence, have revolutionized the way we interact with machines. However, beneath their impressive capabilities lies a fragile vulnerability: adversarial noise. This type of noise is designed to deceive neural networks into misclassifying images, even when the noise is visually imperceptible to the human eye.
What is Adversarial Noise?
Adversarial noise is a carefully crafted disturbance that can be added to an image, making it difficult for neural networks to distinguish between the original and the modified image. This noise is typically imperceptible to humans, but it can have a profound impact on the neural network's decision-making process. By manipulating the input data, attackers can exploit the weaknesses of the neural network and cause it to misclassify images.
Real-World Implications
The consequences of adversarial noise can be far-rea...
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