scholarly journals Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Hameed Siddiqi ◽  
Khurshed Asghar ◽  
Umar Draz ◽  
Amjad Ali ◽  
Madallah Alruwaili ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Image ◽  
Digital Images ◽  
Local Binary Patterns ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Image Forgery Detection

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

scholarly journals Image Forgery Recognition using SWT Method

2019 ◽  
Vol 8 (6) ◽  
pp. 293-296
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Image ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Feature Transformation ◽  
Scale Invariant ◽  
Image Forgery ◽  
Invariant Feature ◽  
Scale Invariant Feature

Using SWT (Stationary Wavelet Change) & SIFT (Scale Invariant Feature Transformation) we attempted to increase the number of features recognized & matched with digital image for forgery identification. Digital image received preferable match for forged area. We collected the forgery area using SIFT& SURF for identification of forgery. We used DWT (Discrete Wavelet Transform) w.r.t. SIFT & SW to subdue absence of translation invariance..

scholarly journals Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1392
Author(s):  
Subramaniam ◽  
Jalab ◽  
Ibrahim ◽  
Mohd Noor
Keyword(s):  
Wavelet Transform ◽  
Fractional Calculus ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Splicing ◽  
Information Carrier ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Redundant Discrete Wavelet Transform

The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.

scholarly journals Digital Image Forgery Detection

2019 ◽  
Vol 8 (2S3) ◽  
pp. 658-661
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Image Manipulation ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Naked Eye ◽  
Image Forgery Detection ◽  
Crime Investigation ◽  
Digital Image Forgery

With the growing challenges in authenticity and integrity of images, image manipulation has crumbled assurance over digital image. The major motivation of the forgery in image is manipulating the image in such a way that it cannot be distinguished to the naked eye. Image manipulation has increased the demand to assess the trustworthiness of digital images when used in crime investigation, as witness of law and for surveillance purposes. In this paper, various types of image forgery and detection techniques have been explained. Initially different kinds of forgery attacks are categorized and summary of passive approach is discussed

scholarly journals IMAGE FORGERY DETECTION USING DYADIC WAVELET TRANSFORM

2013 ◽  
pp. 108-110
Author(s):  
O.M. PRATHIBHA ◽  
N. S. SWATHIKUMARI ◽  
P. SUSHMA
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Input Image ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Forgery ◽  
Dyadic Wavelet ◽  
Image Forgery Detection ◽  
Dyadic Wavelet Transform ◽  
The One

In this paper, we propose a blind copy move image forgery detection method using dyadic wavelet transform (DyWT). DyWT is shift invariant and therefore more suitable than discrete wavelet transform (DWT) for data analysis. First we decompose the input image into approximation (LL1) and detail (HH1) subbands. Then we divide LL1 and HH1 subbands into overlapping blocks and measure the similarity between blocks. The key idea is that the similarity between the copied and moved blocks from the LL1 subband should be high, while the one from the HH1 subband should be low due to noise inconsistency in the moved block. We sort pairs of blocks based on high similarity using the LL1 subband and high dissimilarity using the HH1 subband. Using thresholding, we obtain matched pairs from the sorted list as copied and moved blocks. Experimental results show the effectiveness of the proposed method over competitive methods using DWT and the LL1 or HH1 subbands only.

Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

2020 ◽  
Vol 12 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Nadheer Younus Hussien ◽  
Rasha O. Mahmoud ◽  
Hala Helmi Zayed
Keyword(s):  
Digital Image ◽  
Feature Reduction ◽  
Color Filter ◽  
Forgery Detection ◽  
Image Splicing ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Digital Image Forgery

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

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