1,249 research outputs found
A General Approach for Using Deep Neural Network for Digital Watermarking
Technologies of the Internet of Things (IoT) facilitate digital contents such
as images being acquired in a massive way. However, consideration from the
privacy or legislation perspective still demands the need for intellectual
content protection. In this paper, we propose a general deep neural network
(DNN) based watermarking method to fulfill this goal. Instead of training a
neural network for protecting a specific image, we train on an image set and
use the trained model to protect a distinct test image set in a bulk manner.
Respective evaluations both from the subjective and objective aspects confirm
the supremacy and practicability of our proposed method. To demonstrate the
robustness of this general neural watermarking mechanism, commonly used
manipulations are applied to the watermarked image to examine the corresponding
extracted watermark, which still retains sufficient recognizable traits. To the
best of our knowledge, we are the first to propose a general way to perform
watermarking using DNN. Considering its performance and economy, it is
concluded that subsequent studies that generalize our work on utilizing DNN for
intellectual content protection is a promising research trend
Digital Watermarking for Deep Neural Networks
Although deep neural networks have made tremendous progress in the area of
multimedia representation, training neural models requires a large amount of
data and time. It is well-known that utilizing trained models as initial
weights often achieves lower training error than neural networks that are not
pre-trained. A fine-tuning step helps to reduce both the computational cost and
improve performance. Therefore, sharing trained models has been very important
for the rapid progress of research and development. In addition, trained models
could be important assets for the owner(s) who trained them, hence we regard
trained models as intellectual property. In this paper, we propose a digital
watermarking technology for ownership authorization of deep neural networks.
First, we formulate a new problem: embedding watermarks into deep neural
networks. We also define requirements, embedding situations, and attack types
on watermarking in deep neural networks. Second, we propose a general framework
for embedding a watermark in model parameters, using a parameter regularizer.
Our approach does not impair the performance of networks into which a watermark
is placed because the watermark is embedded while training the host network.
Finally, we perform comprehensive experiments to reveal the potential of
watermarking deep neural networks as the basis of this new research effort. We
show that our framework can embed a watermark during the training of a deep
neural network from scratch, and during fine-tuning and distilling, without
impairing its performance. The embedded watermark does not disappear even after
fine-tuning or parameter pruning; the watermark remains complete even after 65%
of parameters are pruned.Comment: This is a pre-print of an article published in International Journal
of Multimedia Information Retrieval. The final authenticated version is
available online at: https://doi.org/10.1007/s13735-018-0147-1 . arXiv admin
note: substantial text overlap with arXiv:1701.0408
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
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
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Deep Neural Networks have recently gained lots of success after enabling
several breakthroughs in notoriously challenging problems. Training these
networks is computationally expensive and requires vast amounts of training
data. Selling such pre-trained models can, therefore, be a lucrative business
model. Unfortunately, once the models are sold they can be easily copied and
redistributed. To avoid this, a tracking mechanism to identify models as the
intellectual property of a particular vendor is necessary.
In this work, we present an approach for watermarking Deep Neural Networks in
a black-box way. Our scheme works for general classification tasks and can
easily be combined with current learning algorithms. We show experimentally
that such a watermark has no noticeable impact on the primary task that the
model is designed for and evaluate the robustness of our proposal against a
multitude of practical attacks. Moreover, we provide a theoretical analysis,
relating our approach to previous work on backdooring
Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks
We present a method for reading digital data embedded in planar 3D printed
surfaces. The data are organised in binary arrays and embedded as surface
textures in a way inspired by QR codes. At the core of the retrieval method
lies a Convolutional Neural Network, outputting a confidence map of the
location of the surface textures encoding value 1 bits. Subsequently, the bit
array is retrieved through a series of simple image processing and statistical
operations applied on the confidence map. Extensive experimentation with images
captured from various camera views, under various illumination conditions and
from objects printed with various material colours, shows that the proposed
method generalizes well and achieves the level of accuracy required in
practical applications
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
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
Deep Learning in steganography and steganalysis from 2015 to 2018
For almost 10 years, the detection of a hidden message in an image has been
mainly carried out by the computation of Rich Models (RM), followed by
classification using an Ensemble Classifier (EC). In 2015, the first study
using a convolutional neural network (CNN) obtained the first results of
steganalysis by Deep Learning approaching the performances of the two-step
approach (EC + RM). Between 2015-2018, numerous publications have shown that it
is possible to obtain improved performances, notably in spatial steganalysis,
JPEG steganalysis, Selection-Channel-Aware steganalysis, and in quantitative
steganalysis. This chapter deals with deep learning in steganalysis from the
point of view of current methods, by presenting different neural networks from
the period 2015-2018, that have been evaluated with a methodology specific to
the discipline of steganalysis. The chapter is not intended to repeat the basic
concepts of machine learning or deep learning. So, we will present the
structure of a deep neural network, in a generic way and present the networks
proposed in existing literature for the different scenarios of steganalysis,
and finally, we will discuss steganography by deep learning.Comment: Book chapter, final version (October 2019). This chapter will appear
in 2020 in the book titled: "Digital Media Steganography: Principles,
Algorithms, Advances", Book Editor: M. Hassaballah. 46 page
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
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