By: KV Sai Mounish, Department of computer science and technology, Student of computer science and technology, Madanapalle Institute Of Technology and Science, 517325, Angallu, Andhra Pradesh.
ABSTRACT –
Recent advancement in the techniques of deep fake is another major threat to the security and reliability of biometric authentications. Since deepfake can be an AI copy of the person, it is extremely easy to breach the security since the fake replica simply gains access to whatever is prohibited because it is assumed the ‘real’ person is accessing it. This article aims at discussing how deepfakes increase the threat to biometric authentication including facial recognition, voice, and fingerprints recognition. It also discusses existing measures of risk mitigation and stresses that better sensing capability is required to counter new and rapidly developing threats to biometric system’s credibility in a world of active creation of phantom identities.
KEYWORDS –
Deepfakes, Breach, Security, Biometric Authentication, Fingerprint Recognition, Credibility, Advancement.
INTRODUCTION –
Biometric security is nowadays a key element of the modern security solutions actively used in the spheres of finance, healthcare, and the government[1]. These systems utilize various allographical measures of identification like the fingerprints, facial cut, voice etc and are therefore reputed to be more secure than the use of passwords. Nevertheless, with the development of deepfake technology, it is possible to note that new opportunities for biometric authentication’s opponents appeared. These adversaries of deepfakes result from the use of progressives in machine learning and deeply neural networks to create convincing-looking, realistic videos, images, and audio imitating real people. Initially, deepfakes were claimed to be rather fun and exciting and could be used in social networks or movies; however, in recent years, their damaging impacts are apparent all too well. Deep fake has now dawned as a tool in the hand of the hacker, whereby, he can mimic the biometric data and gains control of the system.
This paper focuses on the effects deepfakes pose to biometric security; it examines the way deepfakes challenge the robustness of each biometric modality. It will also cover the current approaches to counter DeepFake based attacks and the severe lack of methodologies to secure Biometric Authentication in the light of emerging IAM threats [2].
Navigating the Threat of Deepfakes
This paper focuses on the suitability of biometric authentication taking into consideration the recent development in deepfake technology. Therefore, deepfakes, realistic and created by means of an artificial intelligence of the probable victims, pose a serious threat to biometric security systems.
Understanding Deepfakes:
Briefing what deepfakes are/ Definition of deepfakes, and how they are created using AI and the relevant subcategories of deep learning and GANs[2].
Subsequently, all the degrees of deepfakes including manipulated videos, images, and voice and the potential uses with regards to them are presented.
Biometric Authentication Systems:
Methods of identification and proper overviews of Biometrics Authentication systems with the present realization of biometrics in over sundry fields.
Description of several biometric systems and brief information on how some of them, for example, facial recognition, vocal recognition, scanning by fingerprints, the IRIS scan industry usually works[3].
The Threat of Deepfakes to Biometric Security: Based on the explanation of deepfake technology and its potential use for fraud, the threats and challenges biometric security faces when mitigating this term are as follows:
Exploding how deep fakes can be used for creating biometric system exposure where the system can be tricked into acknowledging fake biometric details as genuine details.
Other real life scenarios where deepfakes can be potentially used to do the authentication, bypassing the security, thefts and other issues related to cyber crimes.
Current Mitigation Strategies:
Their objectives focused on the assessment of the existing solutions employed to counter the deep fake attacks such as, liveness detection, anti spoofing algorithms, and other deep fake detection methods[4].
The shortcomings of the existing approaches are described, together with the problems that security specialists face in combating constantly improving high-quality deepfakes.
Future Directions and Recommendations:
Discussion on the future necessity of further evolution in the recognition algorithms based on biometrics and deepfake approaches[5]. Suggestions for the enhanced implementation of biometrics security measures, such as MFA, constant supervising, and creation of new guidelines and protocols in regard to deepfake risks. As for the findings related to the research recommendations, stress is being laid on how governments, industries, and researchers can work hand in hand to make the digital world safer than it currently is.
The potential threats and mitigation strategy related is shown in Table 1.
Biometric Modality | Potential Threat from Deepfakes | Mitigation Strategy |
Facial Recognition | Spoofing facial recognition happens | Liveness detection, Anti-spoofing |
Voice Recognition | Synthetic Voice generated to legit users | Voice liveness tests, Acoustic analysis |
Fingerprint Scanning | Fake fingerprint images are created | Multi-factor Authentication, Anti-spoofing |
Iris Recognition | Synthetic iris images created | Infrared scanning, distortion analysis |
Behavioral Biometrics | Manipulation of behavioral patterns | Continuous monitoring, Anomaly detection |
Table 1 : Deepfake threats and mitigation strategies
CONCLUSION –
Biometric open standards are used in security systems to enhance protection in different sorts of industries as an effective substitute for a password. However, the modern developments like deepfake are considered as the severe threats for the effectiveness of such systems. It is probable that deepfakes are the types of fake news that threaten data and systems’ security as deepfakes rely on AI-based machine learning to replicate biometrics.
Thus, as the use of deep fakes as the tool to perform the cybercrime activity increases, the specific protection measures must in carrying out this research, it aims at identifying the actions required to enhance and advance the appropriate safeguard approaches. Today the mechanisms of liveness detection, anti-spoofing algorithms, and detection of deepfakes themselves based on the artificially intelligent mechanisms developed through deep learning are a good defense against deepfakes, but cannot be fixed, knowing that the technology in its nature is rather dynamic. Biometric security is a field that cannot just wait for emergence of new threats as many other fields have done as this is a defense line that has to move forward for the world that constantly attempts the methods of cyber-phoning. In order to foster the creation of distinct leaderships and innovations capable of countering deepfakes’ threats to biometric authentication systems, the heads, researchers, as well as policymakers, must be in harmony. Thus, it becomes possible to draw the conclusion that the protection of dependability of such systems is also not in the sphere of only purely technical matters but it is one of the conditions of trust in modern society, as well as investing into the security.
REFERENCES –
- A. E. Omolara et al., “The internet of things security: A survey encompassing unexplored areas and new insights,” Comput. Secur., vol. 112, p. 102494, Jan. 2022, doi: 10.1016/j.cose.2021.102494.
- S. Khairnar, S. Gite, K. Kotecha, and S. D. Thepade, “Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions,” Big Data Cogn. Comput., vol. 7, no. 1, Art. no. 1, Mar. 2023, doi: 10.3390/bdcc7010037.
- L. Triyono, – Prayitno, M. Rahaman, – Sukamto, and A. Yobioktabera, “Smartphone-based Indoor Navigation for Guidance in Finding Location Buildings Using Measured WiFi-RSSI,” JOIV Int. J. Inform. Vis., vol. 6, no. 4, pp. 829–834, Dec. 2022, doi: 10.30630/joiv.6.4.1528.
- M. Rahaman et al., “Port-to-Port Expedition Security Monitoring System Based on a Geographic Information System,” Int. J. Digit. Strategy Gov. Bus. Transform. IJDSGBT, vol. 13, no. 1, pp. 1–20, Jan. 2024, doi: 10.4018/IJDSGBT.335897.
- P. Yu, Z. Xia, J. Fei, and Y. Lu, “A Survey on Deepfake Video Detection,” IET Biom., vol. 10, no. 6, pp. 607–624, 2021, doi: 10.1049/bme2.12031.
- Mamta, & Gupta, B. (2021). An attribute-based keyword search for m-health networks. Journal of Computer Virology and Hacking Techniques, 17(1), 21-36.
- Khan, A., & Gupta, B. B. (2022). WSNs and IoTs for the Identification of COVID-19 Related Healthcare Issues: A Survey on Contributions, Challenges and Evolution. Security and Privacy Preserving for IoT and 5G Networks: Techniques, Challenges, and New Directions, 225-262.
Cite As
Mounish K.V.S. (2024) Deepfakes impact on Biometric Authentication, Insights2Techinfo, pp.1