  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.
