Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN

Li, Qianwen; Wang, Chengyou; Zhou, Xiao; Qin, Zhiliang

doi:10.1038/s41598-022-19325-y

Cited by 20 publications

(14 citation statements)

References 35 publications

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“…In [22], a customized CNN based on the VGG-16 model was proposed that used transfer learning to increase CMFD accuracy, but it was computationally expensive and took a long time to derive results. The training time was 2.8 hr while the inference time of an image was 0.0532 s. In [23], deep features, such as deep convolution neural networks, were used to perform boundaryto-pixel direction segmentation using the SD-Net method, although it is vulnerable to noise attacks.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Diaa

2024

Eng. Technol. Appl. Sci. Res.

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Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Diaa

2024

Eng. Technol. Appl. Sci. Res.

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“…For copy move, [14] introduced a copy-move forgery detection and localization model based on super boundary-topixel direction (super-BPD) segmentation and deep CNN (DCNN). Starting with employing the segmentation technique that is used to enhance the connection among identical image blocks, thereby improving the accuracy of forgery detection, the DCNN is used to extract image features, ending by using image BPD information to optimize the edges of the rough detected image and obtain the final detected image.…”

Section: A Deep Neural Network Based Image Forgery Detection Techniquesmentioning

confidence: 99%

Enhancing Digital Image Forgery Detection Using Transfer Learning

Khalil,

Ghalwash,

Elsayed

et al. 2023

IEEE Access

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Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it's crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detecting only one type of forgery, such as image splicing or copy-move that is not applied in real life. This paper proposes an approach, to enhance digital image forgery detection using deep learning techniques via transfer learning to uncover two types of image forgery at the same time, The proposed technique relies on discovering the compressed quality of the forged area, which normally differs from the compressed quality of the rest of the image. A deep learning-based model is proposed to detect forgery in digital images, by calculating the difference between the original image and its compressed version, to produce a featured image as an input to the pre-trained model to train the model after removing its classifier and adding a new fine-tuned classifier. A comparison between eight different pre-trained models adapted for binary classification is done. The experimental results show that applying the technique using the adapted eight different pre-trained models outperforms the state-of-the-art methods after comparing it with the resulting evaluation metrics, charts, and graphs. Moreover, the results show that using the technique with the pre-trained model MobileNetV2 has the highest detection accuracy rate (around 95%) with fewer training parameters, leading to faster training time.

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“…Detection of object removal tampering often complements copy-move forgery detection (CMFD) algorithms. Notably, [10] employs super boundary-to-pixel direction (super-BPD) segmentation for image localization and deep CNN (DCNN) (SD-Net) for forgery detection. Pre-trained models like VGG16 have also been integrated into image forgery detection, as [11] demonstrates by employing back-propagation for gradient learning.…”

Section: A Review Of Existing Workmentioning

confidence: 99%

A Comparative Analysis of Local Binary Pattern(LBP) Variants for Image Tamper Detection

Kaur

2023

Preprint

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The proliferation of image tampering in the digital age poses a significant challenge to the authenticity and integrity of visual content. This study presents an approach for detecting image tampering using the Local Binary Pattern (LBP) techniques in conjunction with Convolutional Neural Network (CNN). LBP is a powerful image texture descriptor. The LBP method is employed to extract robust and discriminative features by capturing local texture and intensity patterns from tampered images. These features are then input into a CNN architecture, which is trained using 5-fold cross-validation to ensure generalization and prevent overfitting. A comprehensive benchmark image dataset CASIA-2.0 comprising of 7,541 authentic and 5,124 tampered images is utilized to evaluate the proposed method, and performance evaluation metrics, including accuracy, and confusion matrix, are employed to assess the effectiveness of the system. Experimental results demonstrate the efficiency of the proposed approach over existing state-of-the-art methods, achieving high accuracy in detecting image tampering. A comparative analysis of four types of LBP variants is presented in this work. With circular LBP, Rotation-Invariant LBP, Default, and Uniform LBP we achieved an accuracy of 68%, 72%, 84%, and 96% respectively. This research has significant implications in various domains, including forensic investigations, journalism, and image integrity verification, as it addresses the challenges posed by image tampering, enhancing trust and confidence in digital visual content by ensuring its authenticity and reliability.

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Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN

Cited by 20 publications

References 35 publications

A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Enhancing Digital Image Forgery Detection Using Transfer Learning

A Comparative Analysis of Local Binary Pattern(LBP) Variants for Image Tamper Detection

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