R-LPIPS: An Adversarially Robust Perceptual Similarity Metric

Understand

Similarity metrics have played a significant role in computer vision to capture the underlying semantics of images.

• In recent years, advanced similarity metrics, such as the Learned Perceptual Image Patch Similarity (LPIPS), have emerged.
• These metrics leverage deep features extracted from trained neural networks and have demonstrated a remarkable ability to closely align with human perception when evaluating relative image similarity.
• However, it is now well-known that neural networks are susceptible to adversarial examples, i.e., small perturbations invisible to humans crafted to deliberately mislead the model.