Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks. (arXiv:2205.01714v1 [cs.CL])

Deep learning (DL) is being used extensively for text classification.
However, researchers have demonstrated the vulnerability of such classifiers to
adversarial attacks. Attackers modify the text in a way which misleads the
classifier while keeping the original meaning close to intact. State-of-the-art
(SOTA) attack algorithms follow the general principle of making minimal changes
to the text so as to not jeopardize semantics. Taking advantage of this we
propose a novel and intuitive defense strategy called Sample Shielding. It is
attacker …

adversarial attacks don protect rest text