Spliced Image Detection in 3D Lighting Environments Using Neural Networks

Vinolin, V.; Sucharitha, M.

doi:10.1007/978-981-15-5400-1_15

Cited by 4 publications

(6 citation statements)

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“…Using the proposed algorithm, results were improved in terms of accuracy and execution. Violin et al 23 developed an algorithm for identifying the spliced image based on the illumination characters. For extracting the features, light coefficients are determined using a 3D shape model.…”

Section: Recent Workmentioning

confidence: 99%

Digital image forgery detection under complex lighting using Phong reflection model

Rani

Jain

2022

J. Electron. Imag.

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Image manipulation is transformed into a big issue for data integrity. The use of advanced imaging technology expends the regularity of multimedia forgeries. To detect such forgeries, some effective forgery identification methods are proposed to estimate the 3D lighting fingerprints by making certain suppositions related to the surface and reflection model. Incident lighting dispersal in a scene provides a physics-based key for exposing image exploitations. The proposed technique is more relaxed for multiple light source-based forgery detection. Also, the novelty of the technique is that it considers heterogeneous image surfaces, nearby surface geometry, and texture information to assess the lighting environment. The Phong reflection model is implemented to estimate the structural profiles. The method works in two phases. In the first phase, pre-processing over selected patches is performed followed by angle and error estimations in the second phase. To identify such forgeries, elevation angles ϑ concerning mounted light sources are estimated. The value of the elevation angle is computed for various patches of the image. The proposed method is relevant for a wide range of objects present in the image, i.e., not only limited to head positions, etc. To build up and validate the technique, comprehensive testing on the synthetic image dataset is done. Synthetic images are designed under different intensity light sources. Experimental results demonstrate a better forgery detection accuracy compared with other state-of-the-art methods in this domain. Also, the proposed technique is tested for generalized forged images. A comparative analysis with other related techniques is done to validate the proposed method using a synthetic image dataset. The results are pertinent to all type of images, and an error threshold is limited to 8 degrees with respect to the illuminant position.

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Section: Recent Workmentioning

confidence: 99%

Digital image forgery detection under complex lighting using Phong reflection model

Rani

Jain

2022

J. Electron. Imag.

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“…The light coefficients are extracted from various parts of an image. Using the distance measures, the deviation in these measures is studied to investigate the authenticity of an image (Vinolin and Sucharitha, 2021). The lighting-based detector is further classified into 2D and 3D types.…”

Section: Itp 372mentioning

confidence: 99%

A deep-learning-based image forgery detection framework for controlling the spread of misinformation

Ghai

Kumar

Gupta

2021

ITP

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PurposeWeb users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.Design/methodology/approachThe proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.FindingsThe comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.Research limitations/implicationsThis study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.Practical implicationsThis study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.Social implicationsIn the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.Originality/valueThis study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

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“…Digital media are growing in various domains, and along with that the risk of image tampering is also increasing. It is important to identify such tampering of digital media, and thus, different methods are introduced to process in this concern 1 . But still, finding whether the image is forged or not is a difficult task due to many reasons 2 .…”

Section: Introductionmentioning

confidence: 99%

“…It is important to identify such tampering of digital media, and thus, different methods are introduced to process in this concern. 1 But still, finding whether the image is forged or not is a difficult task due to many reasons. 2 Generally, manipulating will disturb the physical, statistical, geometrical, or intrinsic property of the original image, 3 or the details in the image are misrepresented.…”

Section: Introductionmentioning

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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Spliced Image Detection in 3D Lighting Environments Using Neural Networks

Cited by 4 publications

References 10 publications

Digital image forgery detection under complex lighting using Phong reflection model

Digital image forgery detection under complex lighting using Phong reflection model

A deep-learning-based image forgery detection framework for controlling the spread of misinformation

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

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