Evaluating CNN robustness against various adversarial attacks, including FGSM and PGD.

A comparison analysis between classical and quantum-classical (or hybrid) neural network and the impact effectiveness of a compound adversarial attack.

Hybrid neural network is protected against adversarial attacks using various defense techniques, including input transformation, randomization, and adversarial training.

A quantum-classical (or hybrid) neural network and the use of a adversarial attack mechanism. The core libraries employed are Quantinuum pytket and pytket-qiskit. torchattacks is used for the white-box, targetted, compounded adversarial attacks.

Hybrid neural network model is protected against adversarial attacks using either adversarial training or randomization defense techniques