Spoofing Detection in Images Using Eques Chimp Optimization-based Deep Convolutional Neural Network Model

The geo-position spoofing detection in the images enables the tamper-proof protection of the images to ensure the authenticity of the images. Currently, available techniques for geo-position spoofing detection primarily depend on geo-position data stored in the database. In addition, these techniques are limited in performance due to the shortage of prior information, require more training samples and achieve poor generalization. To address the aforementioned limitations in this research, Eques Chimp Optimization-Deep Convolutional Neural Network (ECO-DCNN) is proposed to enhance the ability of the model to adapt to the evolving spoofing methods, handle noisy images and maintain computational efficiency. The significance of the research relies upon effectively tracing the source cameras by extracting the Photo Response NonUniformity noise (PRNU) from the images, which is then subjected to spoofing detection using Eques Chimp Optimized Deep Neural Network (ECO-DCNN). The ECO is designed by hybridizing the decision-making characters and social aspects of Eques and Chimp in the adaptive parameter tuning of the detection model to guarantee image integrity. Furthermore, for the Training Percentage (TP) analysis, the model exhibits a remarkable performance of 97.00% accuracy, 97.10% sensitivity, 97.19% specificity and 0.97 MCC. Similarly, the K-fold analysis achieves the values with accuracy, sensitivity, specificity and MCC for the model are 96.00%, 96.89%, 95.96% and 0.96%, respectively.