Improvising adversarial attack against prediction of neural network

Fine-tuned resnet34 and mobilenetv2 on the caltech101 dataset. Tested FGSM attacks and used XAI techniques to understand both models behaviours then implemented two defensive measures against the attacks.

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.