Effective near-duplicate image detection using perceptual hashing and deep learning
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Deep learning and transformer-based hashing techniques demand large-scale labeled data and large amounts of training to learn generalizable representations.
“However, similarity is decided by fixed thresholds during deployment and these techniques demand large-scale labeled data and large amounts of training to learn generalizable representations.”
From DBpHash: a blockchain-based dual-band perceptual hashing framework for copyright protection of purely chromatic background images (2026) · cites this paper as [18]
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Nainar, M., Sankaranarayanan, S., & Veeramani, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-54102-1
“Classical pHash with siamese networks and vision transformers are combined with vision transformers to enhance near-duplicate detection under geometric and color transformations 18 .”
- Authors
- Jakhar, Y., & Borah, M. D.
- Affiliations
- National Institute Of Technology Silchar (IN)
- Corresponding author
- Jakhar, Yash
- Borah, Malaya Dutta
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2025-07
- Volume
- 62
- Issue
- 4
- Pages
- 104086
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W4407282393
- Cited by
- 17
- References
- 33
- Citations by year
- 2026: 13 · 2025: 4
- FWCI
- 6.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- near-duplicate image detection, perceptual hashing, Siamese network, vision transformer, metric learning, image similarity
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