1,429 research outputs found
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
BlessMark: A Blind Diagnostically-Lossless Watermarking Framework for Medical Applications Based on Deep Neural Networks
Nowadays, with the development of public network usage, medical information
is transmitted throughout the hospitals. The watermarking system can help for
the confidentiality of medical information distributed over the internet. In
medical images, regions-of-interest (ROI) contain diagnostic information. The
watermark should be embedded only into non-regions-of-interest (NROI) to keep
diagnostic information without distortion. Recently, ROI based watermarking has
attracted the attention of the medical research community. The ROI map can be
used as an embedding key for improving confidentiality protection purposes.
However, in most existing works, the ROI map that is used for the embedding
process must be sent as side-information along with the watermarked image. This
side information is a disadvantage and makes the extraction process non-blind.
Also, most existing algorithms do not recover NROI of the original cover image
after the extraction of the watermark. In this paper, we propose a framework
for blind diagnostically-lossless watermarking, which iteratively embeds only
into NROI. The significance of the proposed framework is in satisfying the
confidentiality of the patient information through a blind watermarking system,
while it preserves diagnostic/medical information of the image throughout the
watermarking process. A deep neural network is used to recognize the ROI map in
the embedding, extraction, and recovery processes. In the extraction process,
the same ROI map of the embedding process is recognized without requiring any
additional information. Hence, the watermark is blindly extracted from the
NROI.Comment: Drs. Soroushmehr and Najarian declared that they had not
contributions to the paper. I removed their name
TRLF: An Effective Semi-fragile Watermarking Method for Tamper Detection and Recovery based on LWT and FNN
This paper proposes a novel method for tamper detection and recovery using
semi-fragile data hiding, based on Lifting Wavelet Transform (LWT) and
Feed-Forward Neural Network (FNN). In TRLF, first, the host image is decomposed
up to one level using LWT, and the Discrete Cosine Transform (DCT) is applied
to each 2*2 blocks of diagonal details. Next, a random binary sequence is
embedded in each block as the watermark by correlating coefficients. In
authentication stage, first, the watermarked image geometry is reconstructed by
using Speeded Up Robust Features (SURF) algorithm and extract watermark bits by
using FNN. Afterward, logical exclusive-or operation between original and
extracted watermark is applied to detect tampered region. Eventually, in the
recovery stage, tampered regions are recovered by image digest which is
generated by inverse halftoning technique. The performance and efficiency of
TRLF and its robustness against various geometric, non-geometric and hybrid
attacks are reported. From the experimental results, it can be seen that TRLF
is superior in terms of robustness and quality of the digest and watermarked
image respectively, compared to the-state-of-the-art fragile and semi-fragile
watermarking methods. In addition, imperceptibility has been improved by using
different correlation steps as the gain factor for flat (smooth) and texture
(rough) blocks
Watermark retrieval from 3D printed objects via synthetic data training
We present a deep neural network based method for the retrieval of watermarks
from images of 3D printed objects. To deal with the variability of all possible
3D printing and image acquisition settings we train the network with synthetic
data. The main simulator parameters such as texture, illumination and camera
position are dynamically randomized in non-realistic ways, forcing the neural
network to learn the intrinsic features of the 3D printed watermarks. At the
end of the pipeline, the watermark, in the form of a two-dimensional bit array,
is retrieved through a series of simple image processing and statistical
operations applied on the confidence map generated by the neural network. The
results demonstrate that the inclusion of synthetic DR data in the training set
increases the generalization power of the network, which performs better on
images from previously unseen 3D printed objects. We conclude that in our
application domain of information retrieval from 3D printed objects, where
access to the exact CAD files of the printed objects can be assumed, one can
use inexpensive synthetic data to enhance neural network training, reducing the
need for the labour intensive process of creating large amounts of hand
labelled real data or the need to generate photorealistic synthetic data
Digital Passport: A Novel Technological Strategy for Intellectual Property Protection of Convolutional Neural Networks
In order to prevent deep neural networks from being infringed by unauthorized
parties, we propose a generic solution which embeds a designated digital
passport into a network, and subsequently, either paralyzes the network
functionalities for unauthorized usages or maintain its functionalities in the
presence of a verified passport. Such a desired network behavior is
successfully demonstrated in a number of implementation schemes, which provide
reliable, preventive and timely protections against tens of thousands of
fake-passport deceptions. Extensive experiments also show that the deep neural
network performance under unauthorized usages deteriorate significantly (e.g.
with 33% to 82% reductions of CIFAR10 classification accuracies), while
networks endorsed with valid passports remain intact.Comment: This paper proposes a new timely IPR solution that embed digital
passports into CNN models to prevent the unauthorized network usage (i.e.
infringement) by paralyzing the networks while maintaining its functionality
for verified user
HiDDeN: Hiding Data With Deep Networks
Recent work has shown that deep neural networks are highly sensitive to tiny
perturbations of input images, giving rise to adversarial examples. Though this
property is usually considered a weakness of learned models, we explore whether
it can be beneficial. We find that neural networks can learn to use invisible
perturbations to encode a rich amount of useful information. In fact, one can
exploit this capability for the task of data hiding. We jointly train encoder
and decoder networks, where given an input message and cover image, the encoder
produces a visually indistinguishable encoded image, from which the decoder can
recover the original message. We show that these encodings are competitive with
existing data hiding algorithms, and further that they can be made robust to
noise: our models learn to reconstruct hidden information in an encoded image
despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and
JPEG compression. Even though JPEG is non-differentiable, we show that a robust
model can be trained using differentiable approximations. Finally, we
demonstrate that adversarial training improves the visual quality of encoded
images
The Robust Digital Image Watermarking using Quantization and Fuzzy Logic Approach in DWT Domain
In this paper a novel approach to embed watermark into the host image using
quantization with the help of Dynamic Fuzzy Inference System (DFIS) is
proposed. The cover image is decomposed up to 3- levels using quantization and
Discrete Wavelet Transform (DWT). A bitmap of size 64x64 pixels is embedded
into the host image using DFIS rule base. The DFIS is utilized to generate the
watermark weighting function to embed the imperceptible watermark. The
implemented watermarking algorithm is imperceptible and robust to some normal
attacks such as JPEG Compression, salt&pepper noise, median filtering, rotation
and cropping.
Keywords: Watermark, Quantization, Dynamic Fuzzy Inference System,
Imperceptible, Robust, JPEG Compression, Cropping.Comment: 7 pages, 11 figures, IJCSN Journa
Neural Imaging Pipelines - the Scourge or Hope of Forensics?
Forensic analysis of digital photographs relies on intrinsic statistical
traces introduced at the time of their acquisition or subsequent editing. Such
traces are often removed by post-processing (e.g., down-sampling and
re-compression applied upon distribution in the Web) which inhibits reliable
provenance analysis. Increasing adoption of computational methods within
digital cameras further complicates the process and renders explicit
mathematical modeling infeasible. While this trend challenges forensic analysis
even in near-acquisition conditions, it also creates new opportunities. This
paper explores end-to-end optimization of the entire image acquisition and
distribution workflow to facilitate reliable forensic analysis at the end of
the distribution channel, where state-of-the-art forensic techniques fail. We
demonstrate that a neural network can be trained to replace the entire photo
development pipeline, and jointly optimized for high-fidelity photo rendering
and reliable provenance analysis. Such optimized neural imaging pipeline
allowed us to increase image manipulation detection accuracy from approx. 45%
to over 90%. The network learns to introduce carefully crafted artifacts, akin
to digital watermarks, which facilitate subsequent manipulation detection.
Analysis of performance trade-offs indicates that most of the gains can be
obtained with only minor distortion. The findings encourage further research
towards building more reliable imaging pipelines with explicit
provenance-guaranteeing properties.Comment: Manuscript + supplement; currently under review; compressed figures
to minimize file size. arXiv admin note: text overlap with arXiv:1812.0151
A Color Image Digital Watermarking Scheme Based on SOFM
Digital watermarking technique has been presented and widely researched to
solve some important issues in the digital world, such as copyright protection,
copy protection and content authentication. Several robust watermarking schemes
based on vector quantization (VQ) have been presented. In this paper, we
present a new digital image watermarking method based on SOFM vector quantizer
for color images. This method utilizes the codebook partition technique in
which the watermark bit is embedded into the selected VQ encoded block. The
main feature of this scheme is that the watermark exists both in VQ compressed
image and in the reconstructed image. The watermark extraction can be performed
without the original image. The watermark is hidden inside the compressed
image, so much transmission time and storage space can be saved when the
compressed data are transmitted over the Internet. Simulation results
demonstrate that the proposed method has robustness against various image
processing operations without sacrificing compression performance and the
computational speed.Comment: International Journal of Computer Science Issues online at
http://www.ijcsi.or
StegaStamp: Invisible Hyperlinks in Physical Photographs
Printed and digitally displayed photos have the ability to hide imperceptible
digital data that can be accessed through internet-connected imaging systems.
Another way to think about this is physical photographs that have unique QR
codes invisibly embedded within them. This paper presents an architecture,
algorithms, and a prototype implementation addressing this vision. Our key
technical contribution is StegaStamp, a learned steganographic algorithm to
enable robust encoding and decoding of arbitrary hyperlink bitstrings into
photos in a manner that approaches perceptual invisibility. StegaStamp
comprises a deep neural network that learns an encoding/decoding algorithm
robust to image perturbations approximating the space of distortions resulting
from real printing and photography. We demonstrates real-time decoding of
hyperlinks in photos from in-the-wild videos that contain variation in
lighting, shadows, perspective, occlusion and viewing distance. Our prototype
system robustly retrieves 56 bit hyperlinks after error correction - sufficient
to embed a unique code within every photo on the internet.Comment: CVPR 2020, Project page: http://www.matthewtancik.com/stegastam
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