CNN-Keypoint Based Two-Stage Hybrid Approach for Copy-Move Forgery Detection
Author:
Affiliation:
1. Department of CE-AI, Marwadi University, Gujarat, Rajkot, India
2. DA-IICT, Gujarat, Gandhinagar, India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10477472.pdf?arnumber=10477472
Reference42 articles.
1. Digital forensics in a post-truth age
2. DCU-Net: a dual-channel U-shaped network for image splicing forgery detection
3. Two-Stage Copy-Move Forgery Detection With Self Deep Matching and Proposal SuperGlue
4. UCM-Net: A U-Net-Like Tampered-Region-Related Framework for Copy-Move Forgery Detection
5. Locality Preserving Projection Based Multiple Copy-Paste Forgery Detection
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1. A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching;International Journal of Machine Learning and Cybernetics;2024-07-03
2. Convolutional Neural Network (CNN) for Fake Logo Detection: A Deep Learning Approach Using TensorFlow Keras API and Data Augmentation;2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT);2024-05-03
3. A Keypoint-Based Two-Step Copy-Move Forgery Detection Technique Using Perceptual Hashing and Hamming Distance;2024
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