6 research outputs found
Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding
This paper presents a novel reversible data hiding (RDH) algorithm for
gray-scaled images, in which the prediction-error of prediction error (PPE) of
a pixel is used to carry the secret data. In the proposed method, the pixels to
be embedded are firstly predicted with their neighboring pixels to obtain the
corresponding prediction errors (PEs). Then, by exploiting the PEs of the
neighboring pixels, the prediction of the PEs of the pixels can be determined.
And, a sorting technique based on the local complexity of a pixel is used to
collect the PPEs to generate an ordered PPE sequence so that, smaller PPEs will
be processed first for data embedding. By reversibly shifting the PPE histogram
(PPEH) with optimized parameters, the pixels corresponding to the altered PPEH
bins can be finally modified to carry the secret data. Experimental results
have implied that the proposed method can benefit from the prediction procedure
of the PEs, sorting technique as well as parameters selection, and therefore
outperform some state-of-the-art works in terms of payload-distortion
performance when applied to different images.Comment: There has no technical difference to previous versions, but rather
some minor word corrections. A 2-page summary of this paper was accepted by
ACM IH&MMSec'16 "Ongoing work session". My homepage: hzwu.github.i
Reversible Embedding to Covers Full of Boundaries
In reversible data embedding, to avoid overflow and underflow problem, before
data embedding, boundary pixels are recorded as side information, which may be
losslessly compressed. The existing algorithms often assume that a natural
image has little boundary pixels so that the size of side information is small.
Accordingly, a relatively high pure payload could be achieved. However, there
actually may exist a lot of boundary pixels in a natural image, implying that,
the size of side information could be very large. Therefore, when to directly
use the existing algorithms, the pure embedding capacity may be not sufficient.
In order to address this problem, in this paper, we present a new and efficient
framework to reversible data embedding in images that have lots of boundary
pixels. The core idea is to losslessly preprocess boundary pixels so that it
can significantly reduce the side information. Experimental results have shown
the superiority and applicability of our work
Watermarking Graph Neural Networks by Random Graphs
Many learning tasks require us to deal with graph data which contains rich
relational information among elements, leading increasing graph neural network
(GNN) models to be deployed in industrial products for improving the quality of
service. However, they also raise challenges to model authentication. It is
necessary to protect the ownership of the GNN models, which motivates us to
present a watermarking method to GNN models in this paper. In the proposed
method, an Erdos-Renyi (ER) random graph with random node feature vectors and
labels is randomly generated as a trigger to train the GNN to be protected
together with the normal samples. During model training, the secret watermark
is embedded into the label predictions of the ER graph nodes. During model
verification, by activating a marked GNN with the trigger ER graph, the
watermark can be reconstructed from the output to verify the ownership. Since
the ER graph was randomly generated, by feeding it to a non-marked GNN, the
label predictions of the graph nodes are random, resulting in a low false alarm
rate (of the proposed work). Experimental results have also shown that, the
performance of a marked GNN on its original task will not be impaired.
Moreover, it is robust against model compression and fine-tuning, which has
shown the superiority and applicability.Comment: https://hzwu.github.io
Ensemble Reversible Data Hiding
The conventional reversible data hiding (RDH) algorithms often consider the
host as a whole to embed a secret payload. In order to achieve satisfactory
rate-distortion performance, the secret bits are embedded into the noise-like
component of the host such as prediction errors. From the rate-distortion
optimization view, it may be not optimal since the data embedding units use the
identical parameters. This motivates us to present a segmented data embedding
strategy for efficient RDH in this paper, in which the raw host could be
partitioned into multiple subhosts such that each one can freely optimize and
use the data embedding parameters. Moreover, it enables us to apply different
RDH algorithms within different subhosts, which is defined as ensemble. Notice
that, the ensemble defined here is different from that in machine learning.
Accordingly, the conventional operation corresponds to a special case of the
proposed work. Since it is a general strategy, we combine some state-of-the-art
algorithms to construct a new system using the proposed embedding strategy to
evaluate the rate-distortion performance. Experimental results have shown that,
the ensemble RDH system could outperform the original versions in most cases,
which has shown the superiority and applicability.Comment: Fig. 1 was updated due to a minor erro