A neural cryptography approach for digital image security using vigenere cipher and tree parity machine
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The document presents a neural cryptography approach for securing digital images using the Vigenère cipher and a tree parity machine (TPM). It addresses the limitations of traditional cryptography methods by proposing an alternative that modifies the key application to enhance visual change in encrypted images. Experimental results indicate that synchronization time in the proposed method is influenced by network size, initial weights, and input vectors.
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Cryptography using artificial neural network
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Performance Analysis of Encryption Algorithm for Network Security on Parallel...
Performance Analysis of Encryption Algorithm for Network Security on Parallel...ijsrd.com The paper analyzes the performance of cryptographic algorithms, specifically the Caesar cipher and RSA, in a parallel computing environment using OpenMP. It demonstrates that parallel execution significantly reduces execution time compared to sequential methods. The findings suggest that enhanced security measures are essential for addressing network security challenges in the future.
IRJET- Machine Learning Application for Data Security
IRJET- Machine Learning Application for Data SecurityIRJET Journal 1) The document proposes a machine learning approach for improving data security through steganography. It hides secret messages in images by modifying pixel values and uses machine learning models like SVM and DNN for smoothing distorted images and retrieving lost data.
2) The method first appends a secret text file onto an image, then performs image-to-image steganography to provide additional security. It decrypts the stego images and uses an SVM for classification and noise removal, along with a DNN network model.
3) Experimental results show that as the size of the hidden text file increases, more noise is introduced, but the machine learning models can effectively smooth the images and retrieve the lost message bits.
Cryptography presentation graduate level
Cryptography presentation graduate levelaKhalid9 The document discusses the applications of neural network-based AI in cryptography, emphasizing its role in enhancing security, detecting vulnerabilities, and improving operational efficiency in various cryptographic protocols. Key applications include optimizing encryption processes, aiding in cryptanalysis, and ensuring resilience against cyber threats, particularly in systems like AES, RSA, and LWE. The study highlights the future importance of AI in developing secure cryptographic systems, especially in the context of quantum computing.
Crptography Electrical engineering .pptx
Crptography Electrical engineering .pptxaKhalid9 This document explores the applications of neural networks in enhancing cryptographic algorithms, highlighting the importance of AI in improving security and efficiency in both symmetric and asymmetric cryptography. Key cryptographic systems discussed include AES, RSA, LWE, and Ascon, with an emphasis on how AI can optimize their security against various attacks. The future of cryptography is positioned to heavily integrate AI, particularly in developing quantum-resistant solutions.
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A neural cryptography approach for digital image security using vigenere cipher and tree parity machine
- 1. A neural cryptography approach for digital image security using Vigenère cipher and tree parity machine M A Budiman, Handrizal, William 1/12/2020 International Conference on Computing and Applied Informatics 2020 Presented by William
- 2. Background Use of cryptography to secure private image Symmetric-key cryptosystem Security depends heavily on secrecy of the key The key distribution has to be done through private channel Public-key cryptosystem Requires high computational power and long computational time the encrypted file size is usually much higher than the original file 1/12/2020
- 3. Research Objective Considering the limitations of both cryptosystems, another method of securing data is necessary. Objective : To find an alternative method to secure images without facing the limitation mentioned earlier Make use of Vigenère Cipher algorithm and Tree Parity Machine neural network 1/12/2020
- 4. Vigenère Cipher One of the classic polyalphabetic substitution cipher. Histrorically used to encrypt alphabetic character. Operates by using shifting mechanism, where each character is shifted by some amount of places. Mathematically, can be written as follows : 1/12/2020
- 5. Problem with the algorithm Due to the repeating nature of the key, the colors of pixels will likely be shifted by the same key value. As a result, there is little visual change between plainimage and cipherimage as can be seen below : Solution ? Multiplying the key value with the y-axis (vertical axis) of the image.
- 6. Tree Parity Machine (TPM) A unique type of neural network in which it has only one output neuron (𝜏) in the output layer. Classified as multi-layer network. Composed of K hidden neuron (𝜎), KxN input neuron (𝑥). Consists of weights (W) whose values ranging from –L to L (synaptic depth)
- 7. The Proposed Method There are 2 main procedures involved in this method: key generation stage and encryption/decrypton.
- 8. Results The simulations were run on an Intel Core i3-4030U 1.90 GHz CPU with 16 GB of RAM. TPM with K = 4, N = 4 and L = 50 This process took about 217 ms to complete and the learning steps required for both TPM to achieve full synchronization state are 4363 steps Key: 49 24 50 50 50 27 50 48 49 50 41 50 50 5 49 50
- 9. Performance In the table below, it can be seen that the larger the size of the network, the longer it will take to synchronize. Similarity of initial chosen weights also affects synchronization time.
- 10. Performance The weights of both TPM are not guaranteed to always be closer to each other. This can be seen from the downward slope in some learning steps. The chance of attractive or repulsive step will occur depends on the randomly generated input vectors. The more the attractive step occurs, the faster the synchronzation time will be and vice versa. In the figure, two TPM with K = 3, N = 12, L = 10, are involved in neural synchronization. The process took 472 steps to complete which is marked by the value of cosine similarity, 1
- 11. Conclusion A neural cryptography approach can be used to enhance the effectiveness of digital image security. The weakness of vigenère cipher can be overcome by multiplying the key value with the y-axis of the image. The result of experiments show that neural synchronization time depends on the size of network, initial weights and randomly generated input vector. 1/12/2020
- 12. References Maqsood F, Ahmed M, Ali M M, Shah M A, "Cryptography: A Comparative Analysis for Modern Techniques," International Journal of Advanced Computer Science and Applications, 2017. Alese B K, Philemon E D, Falaki S O, "Comparative Analysis of Public-Key Encryption Schemes," International Journal of Engineering and Technology, volume 2 No. 9, September, 2012. Dian Rachmawati et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 300 012042 Sowmya D, Priyanka B R, "Design of Word Based Stream Cipher using Tree Parity Machine," Imperial Journal of Interdisciplinary Research, Vol-2, Issue-10, 2016. Pattanayak S, Ludwig S A, “Encryption Based on Neural Cryptography,” (eds) Hybrid Intelligent Systems, Advances in Intelligent Systems and Computing, vol 734, pp. 321–330, March 2018. Fadlil A, Riadi I, Nugrahantoro A, "Kombinasi Sinkronisasi Jaringan Syaraf Tiruan dan Vigenere Cipher untuk Optimasi Keamanan Informasi," Digitalzone: Jurnal Teknologi Informasi dan Komunikasi, vol 11, no 1, May 2020. 1/12/2020
- 13. References Kester Q, "A cryptosystem based on Vigenère cipher with varying key," International Journal of Advanced Research in Computer Engineering & Technology, Vol 1, Issue 10, December 2012. Klein E, Mislovaty R, Kanter I, Ruttor A, Kinzel W, "Synchronization of neural networks by mutual learning and its application to cryptography," Advances in Neural Information Processing Systems, 17, MIT Press, Cambridge, 2005, 689-696. Dolecki M, Ryszard K, "Distribution of the Tree Parity Machine synchronization time," Advances in Science and Technology Research Journal, volume 7, No. 18, June 2013, pp. 20–27. Klimov A, Mityaguine A, Shamir A, "Analysis of Neural Cryptography", Advances in Cryptology—ASIACRYPT, 2003, p288. 1/12/2020