Illumination‐based texture descriptor and fruitfly support vector neural network for image forgery detection in face images

Cristin, R.; Ananth, John Patrick; Raj, V. Cyril

doi:10.1049/iet-ipr.2017.1120

Cited by 25 publications

(11 citation statements)

References 27 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the developed Taylor‐RNet was compared over the methods, like, Random Guess, 12 DeepCNN, 13 Neural network, 16 Kee and Farid's, 18 shape‐from‐shading (SFS) algorithm, 19 Bo Peng et al, 20 Taylor‐ROA DeepCNN, fruitfly optimization algorithm‐support vector neural network (FOA‐SVNN), 21 and CNN‐based mel‐filter bank (MBK) 22 …”

Section: Resultsmentioning

confidence: 99%

Taylor‐RNet: An approach for image forgery detection using Taylor‐adaptive rag‐bull rider‐based deep convolutional neural network

Vinolin

Sucharitha

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Due to the use of powerful computers and advanced software for photo editing, image manipulation in digital images simply degrades the trust in digital images. Image forensic analysis focuses on image authenticity and image content. To process forensic research, different methods are introduced, which effectively differentiate fake images from the original image. A technique named image splicing is commonly used for image tampering, and the tampered image may be used in photography contents, news reports, and so forth, which brings negative influences among the society. Thus, for detecting spliced images, this paper proposed an automatic forgery detection approach named Taylor‐adaptive rag‐bull rider (RR) optimization algorithm‐based deep convolutional neural network (Taylor‐RNet). At first, the face of a human is detected from the spliced image using the Viola Jones algorithm, and later, to estimate light coefficients, the three‐dimensional (3D) shape of the face is determined by using a landmark‐based 3D morphable model (L3DMM). Then, distance measures, like, Bhattacharya, Euclidean, Seuclidean, Chebyshev, correlation coefficients, and Hamming, are determined from the light coefficients that form the feature vector to the proposed Taylor‐RNet, which identifies the spliced image. Taylor‐adaptive RR is the integration of the Taylor series with the adaptive RR optimization algorithm. Finally, the experimental analysis is performed using four data sets, such as DSO‐1, DSI‐1, real data set, and hybrid data set. The analysis result of the proposed method obtained a maximum accuracy of 96.921%, true positive rate of 99.981%, and true negative rate of 99.783%.

show abstract

Section: Resultsmentioning

confidence: 99%

Taylor‐RNet: An approach for image forgery detection using Taylor‐adaptive rag‐bull rider‐based deep convolutional neural network

Vinolin

Sucharitha

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…5.1.3 Comparative methods. The methods used for comparison with the proposed Taylor-ROA DeepCNN include Kee and Farid's (Kee and Farid, 2010), shape from shading (SFS) algorithm (Fan et al, 2012), random guess (Peng et al, 2017), Bo Peng et al (2015), DeepCNN with Adam optimizer (Tu et al, 2017), neural network (Binu and Kariyappa, 2019), fruit fly optimization algorithm-support vector neural network (FOA-SVNN) (Cristin et al, 2018) and convolutional neural network-based motion blur kernel (CNN-based MBK) Song et al (2019), and Kumar and Srivastava (2018).…”

Section: Methodsmentioning

confidence: 99%

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

Vinolin

Sucharitha²

2021

DTA

View full text Add to dashboard Cite

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.

show abstract

“…Most AI-based FIMD schemes are supervised which makes the detection process time and effort consuming [26][27][28].…”

Section: Limitations and Challenges In Fimd Schemes Proposed Mfmd Sch...mentioning

confidence: 99%

“…The face-detected images are utilized to extract features, which are then concatenated to provide the input to the classifier using the Gabor filter, wavelet, and texture operator. Because relevant features and classification algorithms were often manually chosen after running long experiments, the traditional methods in [26,27] mostly relied on handcrafted features, which were inefficient and time-consuming. Another FIMD technique based on a modified CNN model has been created with the aim of increasing accuracy and decreasing complexity.…”

Section: Introductionmentioning

confidence: 99%

A novel blind tamper detection and localization scheme for multiple faces in digital images

Thabit

2023

IET Image Processing

View full text Add to dashboard Cite

Face image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations in digital face images, but they possess individual strengths and limitations. Most schemes can only detect specific manipulations under certain conditions, leading to variable success rates across different images. The literature lacks emphasis on detecting manipulations involving multiple faces. This paper introduces a novel blind tamper detection and localization scheme specifically designed for multiple faces in digital images. The proposed multiple faces manipulation detection (MFMD) scheme consists of two stages: face detection and selection, and image watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated on various multiple‐face images, considering embedding capacity, payload, watermarked image quality, time complexity, and manipulation detection ability. The results demonstrate the MFMD scheme's efficacy in detecting different types of manipulations for multiple faces in images. Furthermore, the watermarked images exhibit high visual quality, even when multiple faces are present. The scheme's efficiency recommends it for practical applications, especially in sharing personal images over unsecured networks. This research advances FIMD techniques by addressing the neglected area of multiple‐face manipulation detection. With improved accuracy, faster processing times, and resilience against various manipulations, the MFMD scheme offers valuable capabilities for enhancing data security and authentication in real‐world scenarios.

show abstract

Illumination‐based texture descriptor and fruitfly support vector neural network for image forgery detection in face images

Cited by 25 publications

References 27 publications

Taylor‐RNet: An approach for image forgery detection using Taylor‐adaptive rag‐bull rider‐based deep convolutional neural network

Taylor‐RNet: An approach for image forgery detection using Taylor‐adaptive rag‐bull rider‐based deep convolutional neural network

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

A novel blind tamper detection and localization scheme for multiple faces in digital images

Contact Info

Product

Resources

About