Identification of Multiple Copy-move Attacks in Digital Images using FFT and CNN

Nair, Gokul G.; Venkatesh, K. S.; Sen, Dipankar; Sonkusare, Reena

doi:10.1109/icccnt51525.2021.9580052

Cited by 3 publications

(1 citation statement)

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“…Based on the changes, edge distortion around the elements when there is copymove, a new method was proposed by combining edge detection on the image and then training a CNN model. This solution achieves an accuracy of 95.91% and proves effective with many copy-move spoofs (Nair et al, 2021). Another method uses CNN to extract dense features of an image by learning the hierarchical features of the forged images.…”

Section: Related Workmentioning

confidence: 95%

An efficient model for copy-move image forgery detection

Huynh

Ly-Tu²,

Le-Tien³

2022

IJWIS

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Purpose This study aims to solve problems of detecting copy-move images. With input images, the problem aims to: Confirm the original or forgery of the images, evaluate the performance of the detection and compare the proposed method’s effectiveness to the related ones. Design/methodology/approach This paper proposes an algorithm to identify copy-move images by matching the characteristics of objects in the same group. The method is carried out through two stages of grouping the objects and comparing objects’ features. The classification and clustering can improve processing time by skipping groups of only one object, and feature comparison on objects in the same group improves accuracy of the detection. YOLO5, the latest version of you only look once (YOLO) developed by Ultralytics LLC, and K-means are applied to classify and group the objects in the first stage. Then, modified Zernike moments (MZMs) and correlation coefficients are used for the features extraction and matching in the second stage. The Open Images V6 data set is used to train the YOLO5 model. The combination of YOLO5 and MZM makes the effectiveness of the proposed method for copy-move image detection with an average accuracy of 94.26% for images of benchmark and MICC-F600 and 95.37% for natural images. The outstanding feature of the method is that it can balance both processing time and accuracy in detecting duplicate regions on the image. Findings The problem is then solved by doing the following steps: Build a method to detect objects and compare their features to find the similarity if they are copy-move objects; use YOLO5 for the object detection and group the same category objects; ignore the group having only one object and extract the features of the other groups by MZMs; detect copy-move regions using K-means clustering; and calculate and compare the detection accuracy of the proposed method and related methods. Originality/value The main contributions of this paper include: Reduce the processing time by using YOLO5 in objects detection and K-means in clustering; improve the accuracy by using MZM to extract features and correlation coefficients to matching them; and implement and prove the effectiveness of the proposed method for three copy-move data sets: benchmark, MICC-F600 and author-built images.

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Section: Related Workmentioning

confidence: 95%