Perceptual hashing is an image deduplication technique that produces hash values reflecting the visual similarity of images, making it effective for identifying duplicate or nearly identical images. Unlike cryptographic hashes, perceptual hashes change minimally for visually similar images, which is beneficial for applications like detecting duplicate memes, moderating content, organizing photo libraries, and avoiding redundant uploads. Implemented in Node.js with the sharp-phash library, perceptual hashing involves computing a hash for each image, comparing these hashes to find duplicates using the Hamming distance, and setting a threshold to identify images as duplicates. This method is efficient for deduplication tasks as it uses small hash sizes and fast comparisons, although it might not handle geometric transformations well and is not suitable for security-critical checks due to potential hash collisions.