ERINet: efficient and robust identification network for image copy-move forgery detection and localization

Ren, Ruyong; Niu, Shaozhang; Jin, Junfeng; Xiong, Keyang; Ren, Hua

doi:10.1007/s10489-022-04104-z

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…To maximize the initial population in the BOA approach, circle chaotic mapping is employed which is expressed in Eq. (12).…”

Section: Chaotic Mappingmentioning

confidence: 99%

“…If a modified image is used in a criminal investigation without the use of a qualified forensic tool, prosecutors may be misled. As a result, a strong image forensics tool for detecting and localizing copied movement is required [12]. Because of the uniform characteristics region of the source and target, image copy-move forgery technology produces a good visual effect and a believable fake result with basic manipulations such as noise addition, JPEG compression, scaling, rotating, and blurring [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network

2024

IJIES

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Copy-move image forgery involves digitally modifying an image by copying and pasting content, hiding foreground objects, or highlighting them through duplication. However, as digital image forgery can become an extremely unsafe and challenging technique to classify images effectively, therefore understanding the detection and classification of real and forgery images is essential. In this research, an improved butterfly optimization algorithmbased convolutional neural network (IBOA-CNN) is proposed for copy-move forgery detection (CMFD), enhancing accuracy and convergence speed by expanding the iteration memory. This proposed approach is used to detect and classify images as original or fake accurately and effectively using deep learning (DL). Initially, the image is obtained by the MICC-F220, MICC-F600, MICC-F2000, and CASIA 2.0 datasets and then image pre-processing is performed by converting Red Green Blue (RGB) into a grayscale image. The Local Binary Pattern (LBP), Wavelet Features (DWT), and ResNet-50 are utilized to extract the features from the images, IBOA is used for feature selection and finally, CNN is employed for the classification to classify CMFD as an original or fake image. Existing methods such as Stacked Sparse Denoising Autoencoder-Spotted Hyena Optimizer-Grasshopper Optimization Algorithm (SSDAE-SHO-GOA), Deep CNN using ResNet-101, and CNN are compared with the IBOA-based CNN approach using MICCF-2000 dataset. The proposed IBOA-based CNN achieves a better accuracy of 97.59%, 99.20%, 99.83%, and 98.92% for MICC-F220, MICC-F600, MICC-F2000, and CASIA 2.0 datasets compared with the existing methods like SSDAE-SHO-GOA, Deep CNN using ResNet-101, and CNN.

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“…To maximize the initial population in the BOA approach, circle chaotic mapping is employed which is expressed in Eq. (12).…”

Section: Chaotic Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network

2024

IJIES

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Applicability of Heavily Compressed JPEG Tampered Images in Social Media and Supervised Machine Learning towards Detection of Forgery

Sarkar,

Roy,

Das

et al. 2024

2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

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ERINet: efficient and robust identification network for image copy-move forgery detection and localization

Cited by 2 publications

References 45 publications

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network

Applicability of Heavily Compressed JPEG Tampered Images in Social Media and Supervised Machine Learning towards Detection of Forgery

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