AES ve RSA Şifrelemesiyle Entegre Edilmiş Otomatik Kodlayıcı ve JPEG Tabanlı Görüntü Sıkıştırma Şemalarının Performans Değerlendirmesi

Abstract

Dijital verinin üretim ve iletim hızındaki artış, hassas içeriklerin korunması ve yetkisiz erişimlerin önlenmesini kritik hâle getirmiştir. Bu nedenle, veri güvenliğini sağlamak için şifreleme yöntemleri zorunlu bir unsur olarak öne çıkmaktadır. Bununla birlikte, artan veri hacmi, bant genişliği ve depolama maliyetlerini minimize etmek amacıyla etkili sıkıştırma tekniklerinin kullanımını gerekli kılmaktadır. Dolayısıyla, güvenli ve verimli veri iletimi için görüntü sıkıştırma ve şifreleme yöntemlerinin bir arada kullanımı kaçınılmaz hâle gelmiştir. Bu çalışma, görüntü verilerinin hem boyutunu azaltmak hem de güvenliğini sağlamak amacıyla hibrit bir sıkıştırma ve şifreleme yaklaşımı önerilmiştir. Sıkıştırma işlemi için otomatik kodlayıcı (Autoencoder, AE) modeli kullanılmıştır. Sıkıştırılmış veriler üzerinde AES ve RSA algoritmaları ile şifreleme gerçekleştirilmiştir. Geliştirilen üç farklı yapı (tekli AE, AE+AES, AE+RSA), görsel kalite, güvenlik, istatistiksel rastgelelik, işlem süresi ve kaynak kullanımı olmak üzere çeşitli metriklerle değerlendirilmiştir. Deneysel çalışmalar, Flickr Yüksek Kaliteli Yüzler veri seti kullanılarak gerçekleştirilmiştir. Deneysel sonuçlar, AE+AES modelinin şifreleme güvenliği ve işlem verimliliği açışından en dengeli yapıyı sunduğunu; AE+RSA modelinin ise daha yüksek güvenlik sağlamakla birlikte daha fazla kaynak tükettiğini ortaya koymuştur. Elde edilen bulgular, derin öğrenme temelli sıkıştırma modellerinin klasik şifreleme algoritmalarıyla entegre edilerek güvenli ve verimli veri iletimi için etkili bir çözüm sunduğunu göstermektedir.

Keywords

• Görüntü şifreleme
• sıkıştırma
• derin öğrenme
• otomatik kodlayıcı
• AES
• RSA

Performance Evaluation of Autoencoder and JPEGBased Image Compression Schemes Integrated with AES and RSA Encryption

Abstract

With the rapid growth of digital data production and transmission, the need to protect sensitive content and avoid unauthorized access has emerged. Data security is achieved through encryption, but the increasing volume of data requires efficient compression techniques to reduce bandwidth and storage expenses. In this paper, the complete framework for the evaluation of deep learning-based Autoencoder (AE) and traditional JPEG compression with symmetric (AES-128) and asymmetric (RSA-2048) cryptographic standards are proposed. A multi-faceted approach is utilized to analyze the performance of four different configurations: AE+AES, AE+RSA, JPEG+AES and JPEG+RSA. This approach includes visual quality (PSNR, SSIM), cryptographic security (NPCR, UACI, Entropy) and statistical randomness and hardware resource utilization. The experimental results on 52,000 high-resolution images from the FFHQ dataset show a large tradeoff between reconstruction fidelity and computational efficiency. JPEG based configurations have better visual reconstruction (PSNR ≈ 44.64 dB) and faster processing speed, but they are highly memory dependent (up to 3.9 GB RAM). In contrast, the proposed AE-based latent encryption method is very lightweight with a small memory footprint (0.01 GB RAM) which makes it an ideal solution for resource-constrained edge devices and IoT applications. The results showed that the deep learning-based compression combined with classical encryption provides a robust and flexible architecture for secure image transmission over different hardware platforms.

Keywords

• Image encryption-Compression
• AES
• RSA
• Autoencoder
• Deep learning

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