3 research outputs found
A Robust Image Watermarking System Based on Deep Neural Networks
Digital image watermarking is the process of embedding and extracting
watermark covertly on a carrier image. Incorporating deep learning networks
with image watermarking has attracted increasing attention during recent years.
However, existing deep learning-based watermarking systems cannot achieve
robustness, blindness, and automated embedding and extraction simultaneously.
In this paper, a fully automated image watermarking system based on deep neural
networks is proposed to generalize the image watermarking processes. An
unsupervised deep learning structure and a novel loss computation are proposed
to achieve high capacity and high robustness without any prior knowledge of
possible attacks. Furthermore, a challenging application of watermark
extraction from camera-captured images is provided to validate the practicality
as well as the robustness of the proposed system. Experimental results show the
superiority performance of the proposed system as comparing against several
currently available techniques
Robust Spatial-spread Deep Neural Image Watermarking
Watermarking is an operation of embedding an information into an image in a
way that allows to identify ownership of the image despite applying some
distortions on it. In this paper, we presented a novel end-to-end solution for
embedding and recovering the watermark in the digital image using convolutional
neural networks. The method is based on spreading the message over the spatial
domain of the image, hence reducing the "local bits per pixel" capacity. To
obtain the model we used adversarial training and applied noiser layers between
the encoder and the decoder. Moreover, we broadened the spectrum of typically
considered attacks on the watermark and by grouping the attacks according to
their scope, we achieved high general robustness, most notably against JPEG
compression, Gaussian blurring, subsampling or resizing. To help us in the
models training we also proposed a precise differentiable approximation of
JPEG.Comment: The article was accepted on TrustCom 2020: The 19th IEEE
International Conference on Trust, Security and Privacy in Computing and
Communication
Collecting the Public Perception of AI and Robot Rights
Whether to give rights to artificial intelligence (AI) and robots has been a
sensitive topic since the European Parliament proposed advanced robots could be
granted "electronic personalities." Numerous scholars who favor or disfavor its
feasibility have participated in the debate. This paper presents an experiment
(N=1270) that 1) collects online users' first impressions of 11 possible rights
that could be granted to autonomous electronic agents of the future and 2)
examines whether debunking common misconceptions on the proposal modifies one's
stance toward the issue. The results indicate that even though online users
mainly disfavor AI and robot rights, they are supportive of protecting
electronic agents from cruelty (i.e., favor the right against cruel treatment).
Furthermore, people's perceptions became more positive when given information
about rights-bearing non-human entities or myth-refuting statements. The style
used to introduce AI and robot rights significantly affected how the
participants perceived the proposal, similar to the way metaphors function in
creating laws. For robustness, we repeated the experiment over a more
representative sample of U.S. residents (N=164) and found that perceptions
gathered from online users and those by the general population are similar.Comment: Conditionally Accepted to ACM CSCW 202