Image Forgery Detection Based on Motion Blur Estimated Using Convolutional Neural Network

Song, Chunhe; Zeng, Peng; Wang, Zhongfeng; Li, Tong; Qiao, Lin; Shen, Li

doi:10.1109/jsen.2019.2928480

Cited by 25 publications

(10 citation statements)

References 28 publications

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“…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

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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%.

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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

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“…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

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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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“…Liu Zhiqiang and others proposed a support vector machine (SVM) fatigue detection model based on the ASL eye tracker [ 3 ]. Wang Lei and others carried out a 36 h sleep deprivation experiment with an eye tracker to determine the thresholds of three fatigue judgment indicators: PERCLOS value, average eye closing time, and yawning frequency [ 4 ]. Nuevo and others used infrared devices to capture the eye movements of moving people and proposed a detection model based on AAM and PCA [ 5 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Motion Fatigue State Detection Based on Neural Networks

Wang

Yang

2022

Computational Intelligence and Neuroscience

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Aiming at the problem of fatigue state detection at the back of sports, a cascade deep learning detection system structure is designed, and a convolutional neural network fatigue state detection model based on multiscale pooling is proposed. Firstly, face detection is carried out by a deep learning model MTCNN to extract eye and mouth regions. Aiming at the problem of eye and mouth state representation and recognition, a multiscale pooling model (MSP) based on RESNET is proposed to train the eye and mouth state. In real-time detection, the state of the eye and mouth region is recognized through the trained convolution neural network model. Finally, the athlete’s fatigue is determined based on PERCLOS and the proposed mouth opening and closing frequency (FOM). The experimental results show that in the training process, we set the batch_size = 100 and the initial learning rate = 0.01. When the evaluation index is no longer improved, the learning rate is reduced by 10 times to 0.001, and a total of 50 epochs are trained. The precision and recall of the system are high. Compared with the infrared image simulating the night state, the RGB image taken by the ordinary camera in the daytime has higher precision and recall. It is proven that the neural network has high detection accuracy, meets the real-time requirements, and has high robustness in complex environments.

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Image Forgery Detection Based on Motion Blur Estimated Using Convolutional Neural Network

Cited by 25 publications

References 28 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

Motion Fatigue State Detection Based on Neural Networks

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