Image Splicing Detection and Localisation using EfficientNet and Modified U-Net Architecture

Ahmed, Md Shamim; Naskar, Ruchira

doi:10.1109/iemecon53809.2021.9689172

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The first stage uses a transfer learning-based algorithm from EfficientNet to classify image regions. At the second stage, the dummy image regions will be trained from the U-Net Architecture network in which the U-Net Encoder part is replaced by EfficientNet layers (Ahmed and Naskar, 2021). The application of preprocessing has been proven to improve the efficiency of image forgery detection.…”

Section: Related Workmentioning

confidence: 99%

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: 99%

An efficient model for copy-move image forgery detection

Huynh

Ly-Tu²,

Le-Tien³

2022

IJWIS

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Digital Image Forgery Detection using Convolutional Neural Network (CNN): A Survey

Ahirwar,

Pandey

2024

2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

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Adaptive residual algorithm for image splicing detection

Ling,

Wenpeng,

Beijing

2024

Journal of Image and Graphics

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机制实现通道间的非线性交互。然后，使用通道注意力 SE (squeeze and excitation) 模块以减少由 ARM 提取残差特征产生的通道之间信息冗余，并以在图像分类领域获得卓越性能的 EfficientNet (high-efficiency network) 为骨干网络，提出一种新的图像拼接检测算法。结果实验结果表明，所提算法在 CASIA I (CASIA image tampering detection evaluation database) ， CASIA II， COLUMBIA COLOR， NIST16 (NIST special database 16) 和 FaceForensic++这 5 个公开数

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Image Splicing Detection and Localisation using EfficientNet and Modified U-Net Architecture

Cited by 3 publications

References 18 publications

An efficient model for copy-move image forgery detection

An efficient model for copy-move image forgery detection

Digital Image Forgery Detection using Convolutional Neural Network (CNN): A Survey

Adaptive residual algorithm for image splicing detection

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