Efficient resampling features and convolution neural network model for image forgery detection

Abstract

The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.

Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt

“…It processes transfer learning more accurately and is capable of being trained with the convolutional layers. Its parameters are tuned and retained for extraction and classification [14], [15]. In computer vision and object detection research fields, the presence of multiple extremely small sized objects in the image poses to be a highly challenging problem for correct object identification.…”

Tampering Detection and Segmentation Model for Multimedia Forensic

“…The resampling attack generally occasionally allows correlation among pixels because of interpolation. The CNN-based [16], [17], image forgery identification model learns resampling features [18] very well using spatial maps produced through translation invariance of various regions of images [19], [20]. Thus, this research work aims to build an efficient resampling feature detection through CNN to detect hybrid attacks and achieve better-tampered region segmentation outcomes [21], [22].…”