Image splicing localization using noise distribution characteristic

Zhang, Depeng; Zhang, Meng; Hu, Jiaojiao

doi:10.1007/s11042-019-7408-8

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…The traditional methods are based on handcrafted features. According to the types of the handcrafted features, the traditional methods can also be divided into four categories: JPEG‐based [ 5 , 6 ] , device‐based [ 7 , 8 ] , post‐processing‐based [ 9 , 10 ] and physically‐based [ 11 , 12 ] . However, the handcrafted features limit the generalization ability of these methods.…”

Section: Introductionmentioning

confidence: 99%

A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization

Chen

Gao

et al. 2021

Chinese J of Electronics

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Recently, Zhou et al. designed a two‐stream faster Region‐Convolutional neural networks (R‐CNN) model RGB‐N for color image splicing localization in CVPR2018. However, the RGB‐N locates spliced regions only at block‐level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two‐stream R‐CNN model is proposed to solve these drawbacks: a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel‐level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi‐scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high‐pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB‐N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.

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Section: Introductionmentioning

confidence: 99%

A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization

Chen

Gao

et al. 2021

Chinese J of Electronics

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“…10 We tested the proposed method on the Columbia dataset (Ng et al 2004) to evaluate the detection accuracy of spliced regions. We took four splicing images from Columbia and tested the detection accuracy rate of our method on these images and the methods proposed by Pan et al (2011), Lyu et al (2014, Zeng et al (2017), Mahdian and Saic (2009) and Zhang et al (2019). Fig.…”

Section: Experimental Analysesmentioning

confidence: 99%

“…The fifth row shows the detection result of the method proposed by Zeng et al (2017), and the sixth row shows the detection of the results of the method proposed by Mahdian and Saic (2009), the spliced regions are marked in the green grid, and falsely detections are marked with a red grid. The seventh row shows the detection result of the method proposed by Zhang et al (2019), and spliced regions are marked with white.…”

Section: Experimental Analysesmentioning

confidence: 99%

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Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

Yildirim

Tahaoglu

Ulutaş

et al. 2023

Wireless Pers Commun

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With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the stateof-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.

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“…A general method of operation detection based on deep learning was proposed by (Bayar, 2016), which detects mean filtering, Gaussian blurring, resampling, and Gaussian white noise image forgery. The existing methods for image visual content tampering and location mainly use cues such as local noise features Zhang et al, 2019) and colour filter array (CFA) mode (Ferrara et al, 2012;Wang, Niu, & Zhang, 2020), to classify specific image patches or pixels in the image as tampered or not tampered, and thus, the ability to locate tampered areas (Bondi, Baroffio, et al, 2017;Huh et al, 2018;Pengpeng et al, 2017). In recent years, deep learning technology has been applied to image tamper detection and has achieved good results.…”

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confidence: 99%

Tampering detection and localization base on sample guidance and individual camera device convolutional neural network features

et al. 2022

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This paper proposes an image tampering detection algorithm based on sample guidance and individual camera device's convolutional neural network (CNN) features (SGICD-CF) to address the challenges in the authenticity and integrity of images. Due to the development of the digital image processing technology, which makes image editing and processing, image tampering and forgery easy and lot simplified, thus solving the problem of image tamper detection, to maintain information security. The principle of SGICD-CF assumes that pixels of the pristine image come from a single camera device, but on the contrary, if an image to be tested is spliced by multiple images from different cameras, then the pixels from the multiple camera devices will be detected. SGICD-CF divides the image to be tested into 64 Â 64 pixel image patches, extracts the camera-related features and some camera model-related information of image patches by source camera identification network (SCI-Net) which is proposed by us, and obtains the classification confidence degree of the image patch. Furthermore, it determines whether the image patch contains foreign pixels according to the obtained confidence degree and finally determines whether the image was tampered according to the classification results of all the image patches, thus locating the tampered area. However, the experimental results show that SGICD-CF can detect and locate the tampered area of an image accurately and our methods have a better performance than other existing methods. Our algorithm can achieve an average correct rate of 0.855 on the synthetic data set based on Dresden, which is higher than other existing detection methods.

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Image splicing localization using noise distribution characteristic

Cited by 13 publications

References 28 publications

A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization

A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization

Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

Tampering detection and localization base on sample guidance and individual camera device convolutional neural network features

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