Papers by Muhammad Ali Babar

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Challenge:	Recent studies have demonstrated that ML Models are sensitive to Adversarial Examples (AEs) AEs are generated by perturbingining examples that preserve the intrinsic utility of the ML solutions but influence the classifier's predictions between original and modified inputs.
Approach:	They propose a reinforcement learning framework that learns a policy that is transferable on unseen datasets and generates utility-preserving and transferable AEs.
Outcome:	The proposed framework is 10% more successful than the state-of-the-art attack TextFooler.