I have a question on image tweaking to trick or fool a model. There are 2 scenarios, 1 to get a higher accuracy (positive tweaking)  and second to get a lower accuracy (negative tweaking) as compared to the trained model. I was thinking to add the gradient of the image to the original image and call it adversarial image. is the concept correct?
like, fake_img = original_img + grad_original image
please help me to understand