Deep Localization of Mixed Image Tampering Techniques

Yancey, Robin Elizabeth

doi:10.48550/arxiv.1904.08484

Cited by 2 publications

(2 citation statements)

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“…Similar to [98], Yancey et al [99] also adopted a Faster R-CNN network with two input streams, where the JPEG compression stream, which was generated from the Block Artifact Grid and Error Level Analysis, replaced the noise stream. The spatial features were selected by the ROI pooling layer from each stream to generate a fixed-length feature vector, which was then used for localization.…”

Section: R-cnn-based Methodsmentioning

confidence: 99%

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Pham

Park

2023

IEEE Access

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In the last decades, deep learning (DL) has emerged as a powerful and dominant technique for solving challenging problems in various fields. Likewise, in the field of digital image forensics, a large and growing body of literature investigates DL-based techniques for detecting and classifying tampered regions in images. This article aims to provides a comprehensive survey of state-of-the-art DL-based methods for image-forgery detection. Copy-move images and spliced images, two of the most popular types of forged images, were considered. Recently, owing to advances in DL, DL-based approaches have yielded much better results as compared to traditional non-DL-based ones. The surveyed techniques were proposed by developing or fusing various efficient DL methods, such as CNN, RCNN, or LSTM to adapt to detecting tampered traces. INDEX TERMSCopy-move image, image forgery, spliced image.

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Section: R-cnn-based Methodsmentioning

confidence: 99%

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Pham

Park

2023

IEEE Access

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“…Ali et al [22] propose a lightweight, deep learning-based system that identifies fake images in the context of double image compression, achieving an overall validation accuracy of 92.23%. Yancey and Davis et al [23] used deep learning and object detection to address both problems, combining multiple techniques for higher accuracy. Their multi-stream faster RCNN network, with the second stream summing the ELA and BAG error maps, achieves even greater precision.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based techniques and error level analysis for image tamper detection

Shetty Sadanand,

Shetty Janardhana,

Purushothaman

et al. 2024

IJEECS

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<span>Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.</span>

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Deep Localization of Mixed Image Tampering Techniques

Cited by 2 publications

References 0 publications

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Convolutional neural network-based techniques and error level analysis for image tamper detection

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