By: Pinaki Sahu, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, 0000pinaki1234.kv@gmail.com
Abstract
User authentication is the first line of defense for sensitive data in the context of dynamic cybersecurity. As the technology and tactics of hostile actors evolve, there is a need for more reliable verification systems. To improve security and user experience, this research explores how to incorporate speech, text and facial recognition into chatbots to create a more authentic approach.
Introduction
Traditional password and PIN-based authentication is vulnerable to brute-force phishing and cyber-attacks. Using biometric data from different services, multiple authentication provides a user-friendly and secure approach. This article aims to integrate voice, face, and text with chatbots to provide a complete and reliable trust mechanism.
Voice Recognition
Accurate and fast authentication is now achievable because of recent developments in voice recognition technology. Chatbots can be equipped with speech recognition software to enable them to identify and comprehend distinct vocal characteristics such as pitch, tone, and cadence. By precisely confirming the user’s identity, speech biometrics adds an extra layer of security to the authentication procedure[1].
Text-based Authentication
Text-based authentication methods, such as pattern recognition and writing style analysis, are incorporated into the multimodal approach. Chatbots are able to analyse user input by considering factors like typing speed, rhythm, and linguistic patterns. The system may get better at distinguishing between attempts by unauthorised users to gain access and those by authorised users by getting to know the subtleties of each person’s communication style[2][3].
Facial Recognition
Facial recognition technology is being used more frequently in chatbots, which reinforces authentication procedures even more. Using the camera of a device, chatbots can take pictures of people’s faces and compare them to templates that have already been stored. This biometric authentication method adds an additional layer of security by including a visual element into the user verification process[3].
Synergy and Integration
The interplay between these separate elements is where multimodal authentication really excels. Chatbots build a more reliable and strong authentication mechanism by fusing text, voice, and facial recognition. Using various modalities at the same time improves accuracy, lowers the possibility of false positives or negatives, and offers a smooth user experience[4].
Fig.1 Voice Recognition, Text-based Authentication and Facial Recognition in Chatbots [4]
Accessibility and User Experience
Any authentication system’s user experience is just as important as its security. Multimodal chatbots provide a user-friendly method of identity verification by balancing security and convenience. This method is inclusive as well, accommodating users of different skills and preferences.
Challenges and Considerations
Although multimodal authentication looks like a feasible option, there are still issues that need to be resolved, including possible biases in recognition algorithms, privacy problems, and ethical issues. For these systems to be implemented successfully, security and user privacy must be adjusted precisely doing so.
Conclusion
In terms of user authentication, the incorporation of voice, text, and face recognition into chatbots is a major advancement. This multimodal strategy offers an effortless and accessible user experience in addition to improving security. Adopting such thorough authentication techniques becomes essential in the continuous defence against cyber threats as technology advances. Organizations can stay ahead of the curve in protecting sensitive data by valuing user security while encouraging new innovation.
References
- Prasad, V. (2015). Voice recognition system: speech-to-text. Journal of Applied and Fundamental Sciences, 1(2), 191.
- Hasal, M., Nowaková, J., Ahmed Saghair, K., Abdulla, H., Snášel, V., & Ogiela, L. (2021). Chatbots: Security, privacy, data protection, and social aspects. Concurrency and Computation: Practice and Experience, 33(19), e6426.
- Barkadehi, M. H., Nilashi, M., Ibrahim, O., Fardi, A. Z., & Samad, S. (2018). Authentication systems: A literature review and classification. Telematics and Informatics, 35(5), 1491-1511.
- Klopfenstein, L. C., Delpriori, S., Malatini, S., & Bogliolo, A. (2017, June). The rise of bots: A survey of conversational interfaces, patterns, and paradigms. In Proceedings of the 2017 conference on designing interactive systems (pp. 555-565).
- Mishra, A., Gupta, B. B., Peraković, D., Yamaguchi, S., & Hsu, C. H. (2021, January). Entropy based defensive mechanism against DDoS attack in SDN-Cloud enabled online social networks. In 2021 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-6). IEEE.
- Gupta, B. B., & Chaturvedi, C. (2019, July). Software defined networking (SDN) based secure integrated framework against distributed denial of service (DDoS) attack in cloud environment. In 2019 International Conference on Communication and Electronics Systems (ICCES) (pp. 1310-1315). IEEE.
- Kumar, A., Shankar, A., Behl, A., Arya, V., & Gupta, N. (2023). Should I share it? Factors influencing fake news-sharing behaviour: A behavioural reasoning theory perspective. Technological Forecasting and Social Change, 193, 122647.
- Sharma, A., Singh, S. K., Badwal, E., Kumar, S., Gupta, B. B., Arya, V., … & Santaniello, D. (2023, January). Fuzzy Based Clustering of Consumers’ Big Data in Industrial Applications. In 2023 IEEE International Conference on Consumer Electronics (ICCE) (pp. 01-03). IEEE.
Cite As
Sahu P. (2023) Multimodal Chat-Bots for Enhanced User Authentication: Integrating Voice, Text, and Facial Recognition, Insights2Techinfo, pp.1