An Overview of Image Duplicacy Detection using Machine Learning

In the era of digital content proliferation, the detection of duplicate images plays a pivotal role in various domains, including copyright protection, content-based image retrieval, and security. This survey paper provides a comprehensive overview of existing techniques for duplicate image detection, ranging from content-based methods to perceptual hashing and deep learning approaches. In addition, we propose a novel methodology that leverages K-Nearest Neighbors (KNN), perceptual hashing, and bit-packed encoding to enhance the accuracy and efficiency of duplicate image detection. Our survey highlights the strengths and limitations of each approach and offers insights into the evolving landscape of this field. This work contributes to the body of knowledge on duplicate image detection and suggests directions for future research.