Several geometrical image modification attackscan be countered if we use image domains thatare invariant under rotation and scaling. Justify it.

Geometrical image modification attacks typically involve altering the orientation or size of an image in order to deceive image recognition systems. These attacks can be particularly effective against systems that have been trained on a specific set of images, as they can cause the system to misidentify or fail to recognize the altered image.

However, if we use image domains that are invariant under rotation and scaling, we can counter these attacks. Here's why:

1. Invariance to Rotation: If an image domain is invariant under rotation, it means that the features of the image remain the same regardless of the orientation of the image. This means that even if an attacker rotates the image, the image recognition system will still be able to correctly identify the image based on its features.

2. Invariance to Scaling: Similarly, if an image domain is invariant under scaling, it means that the features of the image remain the same regardless of the size of the image. This means that even if an attacker resizes the image, the image recognition system will still be able to correctly identify the image based on its features.

By using image domains that are invariant under rotation and scaling, we can ensure that our image recognition systems are robust against geometrical image modification attacks. This is because these systems will be able to correctly identify images based on their features, regardless of the orientation or size of the images.