Digital Image Forensics-Gateway to Authenticity: Crafted with Observations, Trends and Forecasts

Jindal, Neeru; Singh, Kulbir

doi:10.1007/978-3-030-15887-3_33

Cited by 2 publications

(1 citation statement)

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“…Training Phase Then, matching is executed among the block features and finally, the forged area is detected by applying morphological operation [28][29][30].…”

Section: Svm Classifiermentioning

confidence: 99%

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Kaur¹,

Jindal²,

Singh³

2021

Turk J Elec Eng & Comp Sci

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Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images. Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have been proposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously. However, a more efficient method is still in demand to meet the day-today challenges to detect these forgeries at the same time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binary pattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractional parameter of DFrCT is utilized to enhance the accuracy and LBP is used to highlight the tampering artifacts effectively. Then, a support vector machine (SVM) is employed to categorize the images into authentic, copy-move, and spliced images. Next, localization is performed on both the copy-move and spliced images to localize the duplicated areas in the image. Experiments on six benchmark datasets, namely, CASIA v1.0, GRIP, CASIA v2.0, IMD, COVERAGE, and Columbia, attain accuracy rates of 99.67%, 99.23%, 99.76%, 98.81%, 95%, and 98.17%, respectively. To validate the effectiveness of the proposed method, comparative analysis has been performed with existing methods in terms of ROC, precision, recall, F1 score, F2 score, and accuracy. Moreover, the robustness of the proposed work is tested under rotation attack and better results are attained in comparison to the existing techniques.

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“…Training Phase Then, matching is executed among the block features and finally, the forged area is detected by applying morphological operation [28][29][30].…”

Section: Svm Classifiermentioning

confidence: 99%