Identifying the Optimal Deep Learning Based Image Recognition Technique for Dog Breed Detection

Information security is critical in the current digital age, where businesses and organizations heavily rely on technology to run their operations. With the increasing number of cyber threats and attacks, relying only on conventional security measures is insufficient to safeguard sensitive data and computer systems. Machine learning (ML) & artificial intelligence (AI) are essential for enhancing security measures. AI and ML have revolutionized our approach to information security by providing sophisticated analytical capabilities that help identify and respond to threats in real-time. The significance of image recognition technology has grown in information security as it provides a more advanced method for authentication and identification. Image recognition involves using computer algorithms to recognize and classify objects or patterns in photographs. It is used in information security for access control, fraud detection, and intrusion detection, among other purposes. In this research paper, a thorough analysis and study of various deep learning and transfer learning-based image recognition and classification models is conducted to identify the optimal approach for using which modern day information security systems could be designed which could stay one step ahead of the attackers, by making the cryptanalysis process even more difficult for them. In this research, a thorough analysis of Image Recognition models trained on a preprocessed dataset is done and the best model is selected which could be further be used in conjunction with various cryptographic models.