A Median Filtering Forensics CNN Approach Based on Local Binary Pattern

Zhu, Tao; Gu, Hanming; Chen, Zenan

doi:10.1007/978-981-16-6554-7_30

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Please note that despite the median filtering based-approach producing fair results, the approach is vulnerable to easy detection ( Kirchner & Fridrich, 2010 ; Cao et al, 2010 ; Zhu, Gu & Chen, 2022 ). Based on the median filtering characteristics, several hand-crafted features are designed and used along with machine learning algorithms for the detection of the application of the median filter on a digital image.…”

Section: Experimental Analysismentioning

confidence: 99%

“…Based on the median filtering characteristics, several hand-crafted features are designed and used along with machine learning algorithms for the detection of the application of the median filter on a digital image. Examples of the employed features from the filtered image include the gradient between neighboring pixels, the Local Binary Patterns ( Zhu, Gu & Chen, 2022 ), the Fourier Transform coefficients ( Rhee, 2015 ), and the singular values ( Amanipour & Ghaemmaghami, 2019 ). In contrast, the proposed approaches synthesize a newly generated image that is visually imperceptible to a forged image, but its noiseprint is very close to the noiseprint of an authentic image.…”

Section: Experimental Analysismentioning

confidence: 99%

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Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Elliethy

2023

PeerJ Computer Science

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A noiseprint is a camera-related artifact that can be extracted from an image to serve as a powerful tool for several forensic tasks. The noiseprint is built with a deep learning data-driven approach that is trained to produce unique noise residuals with clear traces of camera-related artifacts. This data-driven approach results in a complex relationship that governs the noiseprint with the input image, making it challenging to attack. This article proposes a novel neural noiseprint transfer framework for noiseprint-based counter forensics. Given an authentic image and a forged image, the proposed framework synthesizes a newly generated image that is visually imperceptible to the forged image, but its noiseprint is very close to the noiseprint of the authentic one, to make it appear as if it is authentic and thus renders the noiseprint-based forensics ineffective. Based on deep content and noiseprint representations of the forged and authentic images, we implement the proposed framework in two different approaches. The first is an optimization-based approach that synthesizes the generated image by minimizing the difference between its content representation with the content representation of the forged image while, at the same time, minimizing the noiseprint representation difference from the authentic one. The second approach is a noiseprint injection-based approach, which first trains a novel neural noiseprint-injector network that can inject the noiseprint of an image into another one. Then, the trained noiseprint-injector is used to inject the noiseprint from the authentic image into the forged one to produce the generated image. The proposed approaches are generic and do not require training for specific images or camera models. Both approaches are evaluated on several datasets against two common forensic tasks: the forgery localization and camera source identification tasks. In the two tasks, the proposed approaches are able to significantly reduce several forensic accuracy scores compared with two noiseprint-based forensics methods while at the same time producing high-fidelity images. On the DSO-1 dataset, the reduction in the forensic accuracy scores has an average of 75%, while the produced images have an average PSNR of 31.5 dB and SSIM of 0.9. The source code of the proposed approaches is available on GitHub (https://github.com/ahmed-elliethy/nnt).

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Section: Experimental Analysismentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Elliethy

2023

PeerJ Computer Science

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“…But feature compression method is much more difficult to process, it is complex to solve the feature projection, and the spectral information of the target is reduced after data compression. Nowadays, with the rapid development of neural network technology, it has been widely used in the field of deep learning [3][4][5]. Neural networks can simulate the mechanism of feature extraction at the level of the human brain.…”

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confidence: 99%

Intelligent hyperspectral target detection for reliable IoV applications

Wang

Jin

2022

J Wireless Com Network

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In recent years, hyperspectral imagery has played a significant role in IoV (Internet of Vehicles) vision areas such as target acquisition. Researchers are focusing on integrating detection sensors, detection computing units, and communication units into vehicles to expand the scope of target detection technology with hyperspectral imagery. As imaging spectroscopy technology gradually matures, the spectral resolution of captured hyperspectral images is increasing. At the same time, the volume of data is also increasing. As a result, the reliability of IoV applications is challenged. In this paper, an intelligent hyperspectral target detection method based on deep learning network is proposed. It is based on the residual network structure with the addition of an attention mechanism. The trained network model requires few computational resources and can provide the results in a short time. Our method improves the value of mAP50 by an average of 3.57% for all categories and by up to 5% for a single category on the public dataset.

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Robust Zero Watermarking Algorithm for Medical Volume Data Based on LBP

Liu

et al. 2022

Innovation in Medicine and Healthcare

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A Median Filtering Forensics CNN Approach Based on Local Binary Pattern

Cited by 6 publications

References 17 publications

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Intelligent hyperspectral target detection for reliable IoV applications

Robust Zero Watermarking Algorithm for Medical Volume Data Based on LBP

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