{
  "id": "2209.12391",
  "version": "v1",
  "published": "2022-09-26T03:08:29.000Z",
  "updated": "2022-09-26T03:08:29.000Z",
  "title": "FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs",
  "authors": [
    "Shehzeen Hussain",
    "Nojan Sheybani",
    "Paarth Neekhara",
    "Xinqiao Zhang",
    "Javier Duarte",
    "Farinaz Koushanfar"
  ],
  "comment": "Accepted at ICCAD 2022",
  "doi": "10.1145/3508352.3549357",
  "categories": [
    "cs.CV",
    "cs.AI",
    "cs.AR"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-09-26T03:08:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "accelerating neural steganography",
      "digital watermarking",
      "replacing traditional hand-engineered pipelines",
      "image pixels",
      "watermark encoder neural network"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "ACM",
      "journal": "Commun. ACM"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}