FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs

TL;DR

FastStamp is a novel FPGA-based accelerator platform that significantly speeds up neural network-based image steganography and watermarking, reducing computational overhead while maintaining high success metrics.

Contribution

The paper introduces the first hardware accelerator for DNN-based image steganography and watermarking, featuring a parameter-efficient model and FPGA implementation for high throughput and low power.

Findings

68 times faster inference than GPU implementations

Significantly reduced power consumption

Maintains state-of-the-art watermarking success metrics

Abstract

Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are quickly replacing traditional hand-engineered pipelines. DNN based watermarking techniques have drastically improved the message capacity, imperceptibility and robustness of the embedded watermarks. However, this improvement comes at the cost of increased computational overhead of the watermark encoder neural network. In this work, we design the first accelerator platform FastStamp to perform DNN based steganography and digital watermarking of images on hardware. We first propose a parameter efficient DNN model for embedding recoverable bit-strings in image pixels. Our proposed model can match the success metrics of prior state-of-the-art DNN based watermarking methods while being significantly faster and lighter in terms of memory footprint. We then design an FPGA based accelerator framework to further improve the model throughput and power consumption by leveraging data parallelism and customized computation paths. FastStamp allows embedding hardware signatures into images to establish media authenticity and ownership of digital media. Our best design achieves 68 times faster inference as compared to GPU implementations of prior DNN based watermark encoder while consuming less power.