Coarse-to-fine spatial-channel-boundary attention network for image copy-move forgery detection

Zhong, Junliu; Yang, Jixiang; Gan, Yanfen; Huang, Lianghu; Zeng, Hongyu

doi:10.1007/s00500-022-07432-x

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…The gamma function is employed to convert the integral of integer order (G-L) to fractional order The fractional differential of ∝ -order is denoted in Eq. (13).…”

Section: Fractional Derivativementioning

confidence: 99%

“…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]. Therefore, forged image identification is explored to provide efficient solutions utilizing deep learning algorithms [15].…”

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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“…The gamma function is employed to convert the integral of integer order (G-L) to fractional order The fractional differential of ∝ -order is denoted in Eq. (13).…”

Section: Fractional Derivativementioning

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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“…The network used the DenseNet 41 model to extract deep features, which were classified using the Mask-RCNN [47] to locate tampered regions. Zhong et al [48] proposed a coarse-fine spatial channel boundary attention network and designed the attention module for boundary refinement to obtain finer forgery details and improve the performance of detection. In a few papers [38][39][40][43][44][45], the authors improved the detection performance and robustness of the algorithm but did not distinguish between source and target areas.…”

Section: Image Copy-move Forgery Detectionmentioning

confidence: 99%

Review of Image Forensic Techniques Based on Deep Learning

et al. 2023

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Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.

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“…Attentional Cross-domain CNN Features Splicing DenseNet Zhong [42] Nazir [44] Zhong [46] Dense Link and Feature Reuse…”

Section: Multi-scale Features Fusionmentioning

confidence: 99%

“…The network used the DenseNet 41 model to extract deep features, which were classified using the Mask-RCNN [45] to locate tampered regions. Zhong et al [46] proposed a coarse-fine spatial channel boundary attention network and designed the attention module for boundary refinement to obtain finer forgery details and improve the performance of detection. In [36][37][38][41][42][43], they improve the detection performance and robustness of the algorithm, but do not distinguish between source and target areas.…”

Section: Image Copy-move Forgery Detectionmentioning

confidence: 99%

Review on Image Forensic Techniques Based on Deep Learning

Shi¹,

Chen²,

Wang³

et al. 2023

Preprint

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Digital images have become an important carrier for people to access information in the information age. However, with the development of the technology, digital images are vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics and commonly used datasets for forgery detection are presented. The performance, advantages and disadvantages of existing methods are also compared and analyzed according to different types of detection. In active forensics, robust image watermarking techniques are overviewed, the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are given to provide useful suggestions for people in image forensics and related research fields.

show abstract

Coarse-to-fine spatial-channel-boundary attention network for image copy-move forgery detection

Cited by 6 publications

References 30 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

Review of Image Forensic Techniques Based on Deep Learning

Review on Image Forensic Techniques Based on Deep Learning

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