Comparative Evaluation of Perceptual Hashing and Deep Embedding Methods for Robust and Efficient Image Deduplication

The rapid growth in large-scale image repositories over the past few years has made exact and near-duplicate images increasingly common, creating substantial redundancy that wastes storage resources and reduces retrieval efficiency in practical systems. Even though perceptual hashing and deep learning are promising deduplication strategies, the lack of standardized benchmarks complicates direct comparison. In this study, we conduct a unified, controlled evaluation of five commonly used methods, including four classical perceptual hashes (AHash, DHash, PHash, and WHash) and a CNN-based embedding model. We evaluate all methods on the UKBench and Amazon Berkeley Objects datasets using identical preprocessing, thresholds, and metrics, which include exact duplicates, near-duplicates, and geometrically transformed duplicates. Our experiments highlight a clear trade-off between speed and robustness. Hashing methods are computationally efficient and effective for exact matches, but perform poorly on near-duplicates and under geometric transformations, whereas the CNN model is significantly more robust across all duplicate types, but comes at a high computational cost. Based on these results, we outline practical recommendations for selecting deduplication strategies in large-scale applications. In addition, our evaluation setup serves as a reproducible baseline for future research in image similarity and large-scale deduplication.