Lightweight Models for Real-Time Steganalysis: A Comparisonof MobileNet, ShuffleNet, and EfficientNet

Lightweight Models for Real-Time Steganalysis: A Comparisonof MobileNet, ShuffleNet, and EfficientNet

deep learning; lightweight; steganography;steganalysis; security

In the digital age, the security of communication technologies is paramount, with cybercrime projected to reach $10.5 trillion annually by 2025. While encryption is vital, decrypted data remains vulnerable, prompting the exploration of steganography as an additional security layer. Steganography conceals data within digital media, but its misuse for cyberattacks—such as embedding malware—has highlighted the need for steganalysis, the detection of hidden data. Despite extensive research, few studies have explored lightweight deep-learningmodels for real-time steganalysis in resource-constrained environments like mobile devices. This research evaluates MobileNet, ShuffleNet, and EfficientNet for such tasks, using the BOSSbase-1.01 dataset. Models were assessed based on accuracy, computational efficiency, and resource usage. MobileNet achieved the highest computational speed but with only 63.8% accuracy, falling short of practical application. ShuffleNet and EfficientNetperformed at random-guessing levels with50% accuracy, reflecting the challenges of steganalysis on mobile platforms. Future work aims to improve accuracy by integrating advanced preprocessing techniques, attention mechanisms, and hybrid architectures, as well as leveraging ensemble methods for improved detection. Data augmentation, transfer learning, and hyperparameter tuning will also be explored to optimize model performance. This study contributes by identifying these challenges and offering insights for future research, focusing on optimizing models and preprocessing techniques to enhance detection accuracy in resource-constrained environments

Achmad Bauravindah1*, Dhomas Hatta Fudholi

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6091/990

Master Program inInformatics, Faculty of Industrial Technology, Islamic University of Indonesia, Yogyakarta, Indonesia

26-12-2024

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