"ASCA-SqueezeNet: Detecting Image Forgery with Deep Learning"

What is it about?

The basic element in resolving numerous challenges, particularly social concerns like those in court cases and Facebook, is image forgery detection. The primary objective of this study is to build and develop an effective system for detecting digital image forgery utilising the recently proposed technique called the Aquila Sine Cosine Algorithm (ASCA). The forgery from the digital image is detected in this study using a hybrid deep learning technique that incorporates Deep Convolutional Neural Network (DCNN) and Squeeze Net. Additionally, the training time and computational complexity of the detection process are decreased by updating the weight of both the DCNN and the Squeeze Net using the developed ASCA technique. Additionally, the developed ASCA is produced by combining the update functions of the Aquila Optimizer (AO) with the Sine Cosine Algorithm (SCA). As a result, the hybrid deep learning classifier provides the classified output as either the authentic image or the forged image using a copy-move forgery detection dataset. The experimentation of the developed model has provided higher performance, as shown by testing accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 0.980, 0.976, and 0.956, respectively. Furthermore, by varying the iteration, testing accuracy, TNR, and TPR obtained by the devised technique are 0.944, 0.947, and 0.936, and by varying the population size obtained testing accuracy values of 1, TNR values of 1.003, and TPR values of 0.991, respectively, by algorithmic analysis.

Featured Image

Why is it important?

Copy-move forgery detection is the most important category of image forgery detection. This is accomplished by moving a segment of the image from one location to another within the same image, which may or may not retain some of the original image's noise, color, contrast, or other features. The methods for detecting copy moves are divided into two categories: block dependent algorithms and key point based algorithms

Perspectives

“

In this paper, automatic forgery detection is carried out using an ASCA-based hybrid deep learning algorithm. The developed methodology utilized the dice coefficient for fusing the features that comes out from the congruence coefficient and fisher score. For that, the contourlet transform partitions the filtered image into several sub-bands of images for extracting the features

”