An Overview of Image Duplicacy Detection using Machine Learning

An Overview of Image Duplicacy Detection using Machine Learning

Shreya Moghe1, Vaishnodevi Pardeshi2, Dixita Patel3, Arya Zinje4,  Prof. Nilam Honmane5

1 Student, Information Technology, ZCOER, Pune 411041, India (email: shreyamoghe20@gmail.com)

2 Student, Information Technology, ZCOER, Pune 411041, India (email: vaishnopardeshi29@gmail.com)

3 Student, Information Technology, ZCOER, Pune 411041, India (email: dixitapatel100@gmail.com)

4 Student, Information Technology, ZCOER, Pune 411041, India (email: aryazinje2020@gmail.com)

5 Guide, Information Technology, ZCOER, Pune 411041, India (email: nilam.honmane@zealeducation.com)

ABSTRACT

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.

Keywords: Duplicate Image Detection, Machine Learning, Hashing, Perceptual Hashing, Deep Learning, K-Nearest Neighbors.