IVCVJul 4, 2024

Autoencoded Image Compression for Secure and Fast Transmission

arXiv:2407.03990v24 citationsh-index: 2
Originality Incremental advance
AI Analysis

This addresses the need for secure and fast image transmission in digital applications, though it appears incremental as it builds on existing autoencoder methods.

The paper tackled the problem of secure and efficient image transmission by proposing an autoencoder architecture for compression that inherently encrypts images, achieving an SSIM of 97.5% and an average latency reduction of 87.5%.

With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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