An Image Forensic Technique Based on 2D Lighting Estimation Using Spherical Harmonic Frames

Zhao, Wenyong; Liu, Hong

doi:10.1007/978-3-662-48558-3_33

Cited by 2 publications

(1 citation statement)

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“…Since the estimation of the 3D lightning properties was availed, the techniques of forensics became more powerful, and the forger necessarily needed to adjust the 3D light properties at the time of forging, which stood as a tedious task and consumed more time. In the 2D lightning environment, digital image tampering based on the theory of spherical harmonic (SH) frames is developed by Zhao and Liu (2015). This method employed the details of the image, including the 2D occluding contour, and the lightning feature was estimated using the SH frame.…”

Section: Literature Reviewmentioning

confidence: 99%

Taylor-rider-based deep convolutional neural network for image forgery detection in 3D lighting environment

Vinolin

Sucharitha²

2021

DTA

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PurposeWith the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.Design/methodology/approachThis paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.FindingsExperimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.Originality/valueThe Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

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Section: Literature Reviewmentioning

confidence: 99%