Image Perturbation

Image perturbation involves subtly altering images to understand how these changes affect model performance, explain model predictions, or create adversarial attacks. Current research focuses on developing robust perturbation methods for various tasks, including explanation generation, adversarial attack design, and data protection, often employing techniques like attention mechanisms, generative models (e.g., diffusion models, GANs), and variational autoencoders. These advancements are crucial for improving model explainability, enhancing model security against malicious attacks, and developing more reliable and robust computer vision systems across diverse applications.