Image forgery detection using deep textural features from local binary pattern map

Revi, K. Remya; Wilscy, M.

doi:10.3233/jifs-179720

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Using CNNs for the imagenet large scale visual recognition challenge (ILSVRC) [31] is crucial to come out of deep learning. These CNNs that have already been trained have learned to describe a wide range of images in detail [32]. Hence, this paper uses DenseNet121 as an input layer to pull out the deep embedding texture features from the IR-CLBP-MC image made by the proposed method in this research.…”

Section: Feature Selection and Extraction Using Cnnmentioning

confidence: 99%

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

2023

IJIES

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DeepFake is a concern for celebrities and everyone because it is simple to create. DeepFake images, especially high-quality ones, are difficult to detect using people, local descriptors, and current approaches. On the other hand, video manipulation detection is more accessible than an image, which many state-of-the-art systems offer. Moreover, the detection of video manipulation depends entirely on its detection through images. Many worked on DeepFake detection in images, but they had complex mathematical calculations in preprocessing steps, and many limitations, including that the face must be in front, the eyes have to be open, and the mouth should be open with the appearance of teeth, etc. Also, the accuracy of their counterfeit detection in all previous studies was less than what this paper achieved, especially with the benchmark Flickr faces high-quality dataset (FFHQ). This study proposed, a new, simple, but powerful method called image Re-representation by combining the local binary pattern of multiple-channel (IR-CLBP-MC) color space as an image re-representation technique improved DeepFake detection accuracy. The IR-CLBP-MC is produced using the fundamental concept of the multiple-channel of the local binary pattern (MCLBP), an extension of the original LBP. The primary distinction is that in our method, the LBP decimal value is calculated in each local patch channel, merging them to re-represent the image and producing a new image with three color channels. A pretrained convolutional neural network (CNN) was utilized to extract the deep textural features from twelve sets of a dataset of IR-CLBP-MC images made from different color spaces: RGB, XYZ, HLS, HSV, YCbCr, and LAB. Other than that, the experimental results by applying the overlap and non-overlap techniques showed that the first technique was better with the IR-CLBP-MC, and the YCbCr image color space is the most accurate when used with the model and for both datasets. Extensive experimentation is done, and the high accuracy obtained are 99.4% in the FFHQ and 99.8% in the CelebFaces Attributes dataset (Celeb-A).

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Section: Feature Selection and Extraction Using Cnnmentioning

confidence: 99%

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

2023

IJIES

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“…Wu et al [19] proposed an end-to-end detection network that defined the splice localization problem as a local anomaly-detection problem. Remya and Wilscy [20] used a pretrained convolutional neural network to extract deep texture features from a rotation-invariant local binary pattern (RI-LBP) map of chroma images, and then trained a quadratic support vector machine (SVM), which is a classifier that improves the detection accuracy of fake images. El-Latif et al [21] proposed an image-stitching detection algorithm based on deep learning and wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Fusion Lightweight Image-Splicing Tamper-Detection Model

Zhao

Tian

2022

Electronics

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The easy availability and usability of photo-editing tools have increased the number of forgery attacks, primarily splicing attacks, thereby increasing cybercrimes. Because of an existing image-splicing tamper-detection algorithm based on deep learning with high model complexity and weak robustness, a multiscale fusion lightweight model for image-splicing tamper detection is proposed. For the above problems and to improve MobileNetV2, the structural block of the classification part of the original network structure was removed, the stride of the sixth largest structural block of the network was changed to 1, the dilated convolution was used instead of downsampling, and the features extracted from the second and third large structural blocks in the network were downsampled with maximal pooling; then, the constraint on the backbone network was increased by jumping connections. Combined with the pyramid pooling module, the acquired feature layers were divided into regions of different sizes for average pooling; then, all feature layers were fused. The experimental results show that it had a low number of parameters and required a small amount of computation, achieving 91.0% and 96.4% precision on CASIA and COLUMB, respectively, and 83.2% and 88.1% F-measure on CASIA and COLUMB, respectively.

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SEINet: Semantic-Edge Interaction Network for Image Manipulation Localization

Zhu

Guo

et al. 2021

Pattern Recognition and Computer Vision

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Image forgery detection using deep textural features from local binary pattern map

Cited by 7 publications

References 14 publications

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

A Multiscale Fusion Lightweight Image-Splicing Tamper-Detection Model

SEINet: Semantic-Edge Interaction Network for Image Manipulation Localization

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