<ul><li>Adversarial attacks aim to deceive deep learning models by providing misleading or altered input data, exposing weaknesses in AI systems.</li><li>In Natural Language Processing (NLP), adversarial attacks involve making subtle changes to text that confuse AI models while appearing normal to humans.</li><li>These attacks can occur at the character level, word level, or sentence level, and can be combined for higher effectiveness.</li><li>Generating imperceptible adversarial attacks in NLP is challenging due to the noticeable nature of text alterations.</li></ul>

Understanding Adversarial Attacks in Natural Language Processing

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