Using Fractal Dimension and Singular Values for Image Forgery Detection and Localization

Oommen, Rani Susan; Jayamohan, M.; Sruthy, S

doi:10.1016/j.protcy.2016.05.176

Cited by 13 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In lts in (16)(17)(18), the results confirmed that the forgery detection capability of DWT is high for images. The features of DWT were combined by Jaiswal and Srivastava et al (19) with LBP (Local Binary Pattern) and the classifier for logistic regression, for achieving a considerably better rate of detection of accuracy. A local fractal dimension is used in SVD in (21), the result of which proves that the effectiveness and robustness for detection of multiple forgeries in an image.…”

Section: Review Of Literaturementioning

confidence: 99%

A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

Parashar

Upadhyay

Gupta³

2022

ECS Trans.

View full text Add to dashboard Cite

Image manipulation can cause many ethical, economical, and political issues for everyone. It can be used to create fake information, fake ids, fake online profiles, fake news. A proper technique for image detection and verification is thus, a hot research area, which helps the affected people to overcome the forgery attacks. Due to the increased interaction among people through social networking sites, image forgery is prevalent and it is an essential activity to detect any forged image. This paper’s main objective is to identify fraud or tampered images effectively. By using the methods of copy move forgery detection, the obtained results are more accurate in a minimum time as compared to previous algorithms.

show abstract

Section: Review Of Literaturementioning

confidence: 99%

A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

Parashar

Upadhyay

Gupta³

2022

ECS Trans.

View full text Add to dashboard Cite

show abstract

“…Oommen et al [17] offered a blind or passive approach to CMFD. This approach has seven steps, preprocessing, FE with LFD, image segmentation, organize segment in B+ Tree, Estimate SVD, Image blocks matching using SVD, and Filtering along with highlighting.…”

Section: Related Workmentioning

confidence: 99%

A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images

Velliangiri¹,

Premalatha²

2020

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Different devices in the recent era generated a vast amount of digital video. Generally, it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice. Many kinds of researches on forensic detection have been presented, and it provides less accuracy. This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network (CNN). In the initial stage, the input video is taken as of the dataset and then converts the videos into image frames. Next, perform pre-sampling using the Adaptive Rood Pattern Search (ARPS) algorithm intended for reducing the useless frames. In the next stage, perform preprocessing for enhancing the image frames. Then, face detection is done as of the image utilizing the Viola-Jones algorithm. Finally, the improved Crow Search Algorithm (ICSA) has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network (ECNN) classifier for detecting the forged image frames. The experimental outcome of the proposed system has achieved 97.21% accuracy compared to other existing methods.

show abstract

“…In 2016, Oommen et al [3] have introduced a hybrid model on the basis of singular value decomposition (SVD) and local fractal dimension (LFD) for effective localization and detection of image"s duplicated regions. Initially, the image was segmented as fixed size blocks and has computed the local fractal dimension.…”

Section: Related Workmentioning

confidence: 99%

Face Image Forgery Detection by Weight Optimized Neural Network Model

2019

View full text Add to dashboard Cite

This framework introduces a new automatic image forgery detection approach that involves four main stages like (i) Illumination map computation, (ii) Face detection, (iii) Feature extraction, and (iv) Classification. Initially, the processing of input image is exploited by means of illumination map estimation, which acquires two computation processes called Gray world estimates and Inverse-Intensity chromaticity. Subsequent to this, the Viola-Jones algorithm is employed for the face detection process, which is the second phase, in order to detect the face image clearly. Once after the detection process, the obtained facial image is subjected to feature extraction. For this, Grey Level Co-occurrence Matrix (GLCM) is exploited that extract the facial features from the image. After this, the classification process is carried out for the extracted facial features by employing the Neural Network (NN) classifier. On the whole, this paper mainly concerned over the optimization concept, in which the weight of the NN is optimally selected by using the renowned optimization algorithm named Whale Optimization Algorithm (WOA). To the end, the performance of the implemented model is compared over the other classical models like k-nearest neighbor (kNN), NN and Support Vector Machine (SVM) regarding certain measures like Accuracy, Sensitivity, and Specificity.

show abstract

Using Fractal Dimension and Singular Values for Image Forgery Detection and Localization

Cited by 13 publications

References 6 publications

A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images

Face Image Forgery Detection by Weight Optimized Neural Network Model

Contact Info

Product

Resources

About