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Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption

Muhammed, Rebwar Khalid, Aziz, Ribwar Rashid, Hassan, Alla Ahmad, Aladdin, Aso Mohammed, Saydah, Shaida Jumaah, Rashid, Tarik Ahmed., Hassan, Bryar Ahmad

arXiv.org Artificial Intelligence

Nowadays, cybersecurity has grown into a more significant and difficult scientific issue. The recog-nition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guarantees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an an-swer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryp-tion standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this re-search is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to com-pare the outcomes of the techniques evaluated in this research. The assessment focused on pro-cessing time and speed parameters, examining visual encoding and decoding using Java as the pri-mary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryp-tion and decryption, being over 50% faster than some other algorithms. However, the Twofish algo-rithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.


Hacking Cryptographic Protocols with Advanced Variational Quantum Attacks

Aizpurua, Borja, Bermejo, Pablo, Martinez, Josu Etxezarreta, Orus, Roman

arXiv.org Artificial Intelligence

Here we introduce an improved approach to Variational Quantum Attack Algorithms (VQAA) on crytographic protocols. Our methods provide robust quantum attacks to well-known cryptographic algorithms, more efficiently and with remarkably fewer qubits than previous approaches. We implement simulations of our attacks for symmetric-key protocols such as S-DES, S-AES and Blowfish. For instance, we show how our attack allows a classical simulation of a small 8-qubit quantum computer to find the secret key of one 32-bit Blowfish instance with 24 times fewer number of iterations than a brute-force attack. Our work also shows improvements in attack success rates for lightweight ciphers such as S-DES and S-AES. Further applications beyond symmetric-key cryptography are also discussed, including asymmetric-key protocols and hash functions. In addition, we also comment on potential future improvements of our methods. Our results bring one step closer assessing the vulnerability of large-size classical cryptographic protocols with Noisy Intermediate-Scale Quantum (NISQ) devices, and set the stage for future research in quantum cybersecurity.


China selling deadly AI 'Blowfish' drones that decide who lives and who dies to Middle East war zones

#artificialintelligence

CHINA is selling deadly'Blowfish' drones which can decide who lives and who dies to armies in the war-torn Middle East, say reports. The unmanned war machines are capable of launching autonomous strikes with their arsenal of mortar shells, grenade launchers and machine guns. They are said to be "impossible to defend" against and the Pentagon has already made it clear it fears they will end up in the wrong hands. Some military experts fear the proposed sale of the AI mini-choppers will spark even more bloodshed in the troubled region, reports news.com. "They would be impossible to defend yourself against," warns University of New South Wales Professor of Artificial Intelligence Toby Walsh.