Hybrid Attention-GAN Framework for Secure Data Encryption and Decryption: Leveraging Transformer-based Attention Mechanisms and Adversarial Learning
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| Format: | Recurso digital |
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Zenodo
2024
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| _version_ | 1866901608459141120 |
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| author | s, Sangheethaa |
| author_facet | s, Sangheethaa |
| contents | <p>This paper introduces a novel Hybrid Attention-GAN Framework that integrates transformer-based attention mechanisms with generative adversarial learning to perform secure data encryption and decryption. The proposed model employs a Transformer encoder to capture complex dependencies in plaintext and a GAN generator to produce highly obfuscated ciphertext that closely resembles random noise. On the decryption side, a self-supervised Transformer decoder reconstructs the original data without needing explicit plaintext-ciphertext pairs, enhancing both adaptability and security. The key contributions include:</p> <ul> <li> <p>The first dual-phase encryption-decryption architecture combining Transformers and GANs.</p> </li> <li> <p>Use of self-supervised learning for decryption, significantly improving accuracy while reducing training overhead.</p> </li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15193574 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid Attention-GAN Framework for Secure Data Encryption and Decryption: Leveraging Transformer-based Attention Mechanisms and Adversarial Learning s, Sangheethaa <p>This paper introduces a novel Hybrid Attention-GAN Framework that integrates transformer-based attention mechanisms with generative adversarial learning to perform secure data encryption and decryption. The proposed model employs a Transformer encoder to capture complex dependencies in plaintext and a GAN generator to produce highly obfuscated ciphertext that closely resembles random noise. On the decryption side, a self-supervised Transformer decoder reconstructs the original data without needing explicit plaintext-ciphertext pairs, enhancing both adaptability and security. The key contributions include:</p> <ul> <li> <p>The first dual-phase encryption-decryption architecture combining Transformers and GANs.</p> </li> <li> <p>Use of self-supervised learning for decryption, significantly improving accuracy while reducing training overhead.</p> </li> </ul> |
| title | Hybrid Attention-GAN Framework for Secure Data Encryption and Decryption: Leveraging Transformer-based Attention Mechanisms and Adversarial Learning |
| url | https://doi.org/10.5281/zenodo.15193574 |