Brand.dev is developing an API to fetch comprehensive company brand data from any domain, including logos and colors, using a single API call. A significant challenge they have encountered is the need to efficiently deduplicate logos due to the high volume of images processed daily. They have implemented perceptual hashing, a technique for creating "fuzzy fingerprints" of media files, which generates similar hash values for visually similar images, unlike cryptographic hashes that change drastically with minor input alterations. This makes perceptual hashing ideal for identifying duplicate or near-duplicate images. The pHash algorithm, a popular method, involves resizing and normalizing images, performing a Discrete Cosine Transform, and creating a binary pattern that encapsulates the image's appearance. This technique is applied in real-world scenarios such as social media duplicate detection, content moderation, photo library management, and cloud storage deduplication. By comparing the Hamming distance between perceptual hashes, systems can quantitatively measure image similarity and efficiently manage duplicates. While perceptual hashing is powerful for detecting visual similarities, it is not suitable for security-critical applications due to the potential for hash collisions. Developers are encouraged to use perceptual hashing alongside cryptographic hashes to achieve both exact and similar image deduplication.