Auguring Fake Face Images Using Dual Input Convolution Neural Network

Abstract

Deepfake technology uses auto-encoders and generative adversarial networks to replace or artificially construct fine-tuned faces, emotions, and sounds. Although there have been significant advancements in the identification of particular fake images, a reliable counterfeit face detector is still lacking, making it difficult to identify fake photos in situations with further compression, blurring, scaling, etc. Deep learning models resolve the research gap to correctly recognize phony images, whose objectionable content might encourage fraudulent activity and cause major problems. To reduce the gap and enlarge the fields of view of the network, we propose a dual input convolutional neural network (DICNN) model with ten-fold cross validation with an average training accuracy of 99.36 ±0.62, a test accuracy of 99.08 ± 0.64, and a validation accuracy of 99.30 ± 0.94. Additionally, we used ’SHapley Additive exPlanations (SHAP) ’ as explainable AI (XAI) Shapely values to explain the results and interoperability visually by imposing the model into SHAP. The proposed model holds significant importance for being accepted by forensics and security experts because of its distinctive features and considerably higher accuracy than state-of-the-art methods.

CO‐WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

“…Furthermore, the Gaussian map is imbued with better ergodicity and mixing properties, enabling it to cover the search space thoroughly and uniformly. Thus, employing the Gaussian chaotic map in COWOA can augment its ability to locate the global optima and optimize its overall performance [39–41] …”

“…Thus, employing the Gaussian chaotic map in COWOA can augment its ability to locate the global optima and optimize its overall performance. [39][40][41] Step 1: A given network structure is first constructed using a given hidden layer, a given number of nodes, and an absurd measure of search agents. The input values, the quantity of hidden layer nodes, and number of output layers all affect how many weights and biases are applied to the control parameters.…”

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

“…The CNN proved its capacity to create an internal representation of the two-dimensional images, which enabled the model to represent specific locations and scales of various image features, using different elements of AI, including deep-fake [49], medicine [36,50], agriculture [51] etc.…”

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

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