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

Subramaniam, Thamarai; Jalab, Hamid A.; Ibrahim, Rabha W.; Noor, Nurul Fazmidar Mohd

doi:10.3390/sym11111392

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…For forged image detection involving conformable focus measures model for feature extraction, Subramaniam et al [13] proposed a combination of feature extractions in "redundant discrete wavelet transforms (RDWT)" to enhance the image forgery detection. By combining conformable focus measurements and focus measure operators, the study enhanced the splicing detection accuracy up to 98.6% with 24 extracted feature vectors.…”

Section: Related Workmentioning

confidence: 99%

Image Splicing Detection Using Generalized Whittaker Function Descriptor

Băleanu¹,

Al-Shamayleh²,

Ibrahim³

2023

Computers, Materials &Amp; Continua

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Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the "support vector machine" (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel. When tested on publicly available image dataset "CASIA" v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 × 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.

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Section: Related Workmentioning

confidence: 99%

Image Splicing Detection Using Generalized Whittaker Function Descriptor

Băleanu¹,

Al-Shamayleh²,

Ibrahim³

2023

Computers, Materials &Amp; Continua

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“…Images were manipulated historically as a political propaganda tool to advance a certain political agenda. Tampering emerged as a result of the greater reliance on visual images as the primary means of communicating core ideas [6].…”

Section: Forgery Imagementioning

confidence: 99%

Detection of Image Tempering: Conventional and Deep Learning-based Techniques

Da. Khallaf,

Hanon Alasadi1,2

2023

JEPS

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In light of the cumulative use of digital images in a wide range of apps, as well as the accessibility ofimage manipulation software, detecting image alteration has become a difficult task. The copy-movetechnique is the most commonly used sort of picture counterfeiting. A portion of an image is duplicatedand manipulated in various ways. Handcrafted qualities are commonly used in the identification ofpicture forgery and counterfeiting. It has always been a difficulty with earlier photo reproductiondetection systems that they would rather only detect a specific type of tampering if they are aware ofspecific image features. Deep learning is currently being used to detect image alteration, which is abreakthrough. These strategies were even more efficient than prior methods since they were able toextract complex images from them. The purpose of this work is to teach you about deep learning-basedpicture forgery detection methods, why they function, and how they can be improved upon. In addition,you will learn about publicly available image forgery datasets.

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“…where x presents the pixel value in the direction of x −axis (similarly, for y-axis), and C (α) υ in refers to the fractal conformable variation of the individual function. The fractal conformable operators showed a significant improvement in the application of image processing (see [27][28][29]). In this effort, we combined these operators to bring a hybrid differential operator.…”

Section: Fractal Flame In Terms Of Lcdomentioning

confidence: 99%