Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

Abstract

Visual representation of synthetic images is very accurate, due to which it is difficult to differentiate them for their natural counterparts. Existing models that perform this differentiation are either very complex, or cannot be scaled for multidomain image sets. Moreover, the accuracy of these models depends directly upon type of dataset & feature sets used for training & validation purposes. To overcome these limitations, this paper proposes design of a Hybrid GWO CNN Model for identification of Synthetic Images for big data Applications. The proposed model initially extracts multidomain feature sets from input images, that includes wavelet, cosine, fourier & convolutional features. These features are processed via a grey wolf optimization (GWO) Model, that assists in improving inter-class feature variance while minimizing intra-class variance levels. The GWO Model identifies training & validation sets, thereby assisting the classification model to accurately differentiate between different image types. To perform this task, a variance-based fitness function was modelled that covers both inter-class & inter-class variance levels. This classification is performed via use of a transfer learning-based CNN Model, that extends VGG-19 for high-efficiency operations. The proposed model was tested on a large number of datasets including Synthetic Fruit, Unsplash, ESPL Synthetic Image, and Okazaki Synthetic Texture Image (OSTI) databases. Based on these datasets, accuracy, precision, computational delay, recall & AUC (Area Under the Curve) metrics were evaluated & compared with existing models. It was observed that the proposed model showcased 9.5% better accuracy, 8.3% higher precision, 6.5% better recall, and 3.9% faster performance when compared with existing models. Due to such high performance under different datasets, the proposed model is useful for large-scale synthetic-image identification use cases.

AMVAFEx: Design of a Multispectral Data Representation Engine for Classification of EEG Signals via Ensemble Models

“…However, these models only have a performance in moderately accurate way that is improvised in the work of [17,18]. In these works, a progressive discriminative scanty portrayal classifier, a time area successive elements order utilizing a LSTM neural organization, and a DCNN neural organization are investigated [19]the quality of EEG data can vary significantly, and existing models may not always handle data of varying quality effectively. This work can help improve the accuracy of these models.…”