# Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism

arXiv: https://arxiv.org/abs/2604.20245 · HTML: https://mlsanity.com/paper/2604.20245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/1901.07821/) (2019)
- [Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters](https://mlsanity.com/paper/1810.00440/) (2018)
- [Universal Rate-Distortion-Perception Representations for Lossy Compression](https://mlsanity.com/paper/2106.10311/) (2021)
- [Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding](https://mlsanity.com/paper/2010.01185/) (2020)
- [A coding theorem for the rate-distortion-perception function](https://mlsanity.com/paper/2104.13662/) (2021)
- [Optimal Compression of Locally Differentially Private Mechanisms](https://mlsanity.com/paper/2111.00092/) (2021)
- [The Rate-Distortion-Perception Tradeoff: The Role of Common Randomness](https://mlsanity.com/paper/2202.04147/) (2022)
- [Compression with Exact Error Distribution for Federated Learning](https://mlsanity.com/paper/2310.20682/) (2023)
- [Secure Deep-JSCC Against Multiple Eavesdroppers](https://mlsanity.com/paper/2308.02892/) (2023)
- [Adaptive Compression in Federated Learning via Side Information](https://mlsanity.com/paper/2306.12625/) (2023)
- [The Rate-Distortion-Perception Trade-off: The Role of Private Randomness](https://mlsanity.com/paper/2404.01111/) (2024)
- [Timely and Massive Communication in 6G: Pragmatics, Learning, and Inference](https://mlsanity.com/paper/2306.17580/) (2023)
- [Rate-Distortion Theory for Secrecy Systems](https://mlsanity.com/paper/1305.3905/) (2013)
- [Secure Multi-Function Computation with Private Remote Sources](https://mlsanity.com/paper/2106.09485/) (2021)
- [SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks](https://mlsanity.com/paper/2511.01466/) (2025)
- [Randomized Distributed Function Computation (RDFC): Ultra-Efficient Semantic Communication Applications to Privacy](https://mlsanity.com/paper/2603.09577/) (2026)
- [Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation](https://mlsanity.com/paper/2603.10750/) (2026)

## Abstract (arXiv)

Fundamental rate-distortion-perception (RDP) trade-offs arise in applications requiring maintained perceptual quality of reconstructed data, such as neural image compression. When compressed data is transmitted over public communication channels, security risks emerge. We therefore study secure RDP under negligible information leakage over both noiseless channels and broadcast channels, BCs, with correlated noise components. For noiseless channels, the exact secure RDP region is characterized. For BCs, an inner bound is derived and shown to be tight for a class of more-capable BCs. Separate source-channel coding is further shown to be optimal for this exact secure RDP region with unlimited common randomness available. Moreover, when both encoder and decoder have access to side information correlated with the source and the channel is noiseless, the exact RDP region is established. If only the decoder has correlated side information in the noiseless setting, an inner bound is derived along with a special case where the region is exact. Binary and Gaussian examples demonstrate that common randomness can significantly reduce the communication rate in secure RDP settings, unlike in standard rate-distortion settings. Thus, our results illustrate that random binning-based coding achieves strong secrecy, low distortion, and high perceptual quality simultaneously.