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Analysis of Vulnerabilities in Autonomous Driving Environments through Physical Adversarial Attacks Incorporating Natural Elements
Kyuchan Cho, Woosang Im, Sooyong Jeong, Hyunil Kim, Changho Seo
http://doi.org/10.5626/JOK.2024.51.10.935
Advancements in artificial intelligence technology have significantly impacted the field of computer vision. Concurrently, numerous vulnerabilities related to adversarial attacks, which are techniques designed to force models into misclassification, have been discovered. In particular, adversarial attacks such as physical adversarial attacks in the real world, pose a serious threat to autonomous vehicle systems. These attacks include artificially created attacks such as adversarial patches and attacks that exploit natural elements to cause misclassification. A common scenario in autonomous driving environments involves obstruction of traffic signs by natural elements such as fallen leaves or snow. These elements do not remain stationary. They can cause misclassification even in fleeting moments, highlighting a critical vulnerability. Therefore, this study investigated adversarial patch attacks based on natural elements, proposing fallen leaves as a natural adversarial element. Specifically, it reviewed current trends in adversarial attack research, presented an experimental environment based on natural elements, and analyzed experimental results to assess vulnerabilities posed by fallen leaves in physical environments to autonomous vehicles.
Practically Secure Key Exchange Scheme based on Neural Network
Sooyong Jeong, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2019.46.2.208
Key exchange is one of the major aspects in cryptography. Recently, compared to the existing key exchange schemes, more efficient key exchange schemes have been proposed based on neural network learning. After the first key exchange scheme based on neural network was proposed, various attack models have been suggested in security analysis. Hebbian learning rule is vulnerable to majority attack which is the most powerful attack. Anti Hebbian learning rule is secure against majority attack has a limitation in efficiency, so we can only use key exchange scheme based on random walk learning rule which is more secure and efficient than the others. However, if we use random walk learning rule, the efficiency which is advantage about neural cryptography is reduced than the other learning rules. In this paper we analyze random walk and neural cryptography, and we propose new learning rule which is more efficient than existing random walk learning rule. Also, we theoretically analyze about key exchange scheme which is uses new learning rule and verify the efficiency and security by implementing majority attack model.
Secure Format-Preserving Encryption for Message Recovery Attack
Sooyong Jeong, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2017.44.8.860
Recently, due to the personal information security act, the encryption of personal information has attracted attention. However, if the conventional encryption scheme is used directly, the database schema must be changed because the conventional encryption scheme does not preserve the format of the data, which can yield a large cost. Therefore, the Format-Preserving Encryption(FPE) has emerged as an important technique that ensures the confidentiality of the data and maintains the database schema naturally. Accordingly, National Institute of Standards and Technology(NIST) recently published the FF1 and FF3 as standards for FPE, although problems have been found in the security of FF1 and FF3 against message recovery attacks. In this paper, we study and analyze FF1 and FF3 as the standards of FPE, as well as the message recovery attack on these schemes. We also study a secure FPE against message recovery attack and verify the efficiency by implementing standardized FF1 and FF3.
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