260 research outputs found
Delay-Dependent Robust Exponential Stability and H
This paper deals with the problem of robust exponential stability and H∞ performance analysis for a class of uncertain Markovian jumping system with multiple delays. Based on the reciprocally convex approach, some novel delay-dependent stability criteria for the addressed system are derived. At last, numerical examples is given presented to show the effectiveness of the proposed results
Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs
We propose a novel multi-bit box-free watermarking method for the protection
of Intellectual Property Rights (IPR) of GANs with improved robustness against
white-box attacks like fine-tuning, pruning, quantization, and surrogate model
attacks. The watermark is embedded by adding an extra watermarking loss term
during GAN training, ensuring that the images generated by the GAN contain an
invisible watermark that can be retrieved by a pre-trained watermark decoder.
In order to improve the robustness against white-box model-level attacks, we
make sure that the model converges to a wide flat minimum of the watermarking
loss term, in such a way that any modification of the model parameters does not
erase the watermark. To do so, we add random noise vectors to the parameters of
the generator and require that the watermarking loss term is as invariant as
possible with respect to the presence of noise. This procedure forces the
generator to converge to a wide flat minimum of the watermarking loss. The
proposed method is architectureand dataset-agnostic, thus being applicable to
many different generation tasks and models, as well as to CNN-based image
processing architectures. We present the results of extensive experiments
showing that the presence of the watermark has a negligible impact on the
quality of the generated images, and proving the superior robustness of the
watermark against model modification and surrogate model attacks
Supervised GAN Watermarking for Intellectual Property Protection
We propose a watermarking method for protecting the Intellectual Property
(IP) of Generative Adversarial Networks (GANs). The aim is to watermark the GAN
model so that any image generated by the GAN contains an invisible watermark
(signature), whose presence inside the image can be checked at a later stage
for ownership verification. To achieve this goal, a pre-trained CNN
watermarking decoding block is inserted at the output of the generator. The
generator loss is then modified by including a watermark loss term, to ensure
that the prescribed watermark can be extracted from the generated images. The
watermark is embedded via fine-tuning, with reduced time complexity. Results
show that our method can effectively embed an invisible watermark inside the
generated images. Moreover, our method is a general one and can work with
different GAN architectures, different tasks, and different resolutions of the
output image. We also demonstrate the good robustness performance of the
embedded watermark against several post-processing, among them, JPEG
compression, noise addition, blurring, and color transformations
General GAN-generated image detection by data augmentation in fingerprint domain
In this work, we investigate improving the generalizability of GAN-generated
image detectors by performing data augmentation in the fingerprint domain.
Specifically, we first separate the fingerprints and contents of the
GAN-generated images using an autoencoder based GAN fingerprint extractor,
followed by random perturbations of the fingerprints. Then the original
fingerprints are substituted with the perturbed fingerprints and added to the
original contents, to produce images that are visually invariant but with
distinct fingerprints. The perturbed images can successfully imitate images
generated by different GANs to improve the generalization of the detectors,
which is demonstrated by the spectra visualization. To our knowledge, we are
the first to conduct data augmentation in the fingerprint domain. Our work
explores a novel prospect that is distinct from previous works on spatial and
frequency domain augmentation. Extensive cross-GAN experiments demonstrate the
effectiveness of our method compared to the state-of-the-art methods in
detecting fake images generated by unknown GANs
Multiobjective nonfragile fuzzy control for nonlinear stochastic financial systems with mixed time delays
In this study, a multiobjective nonfragile control is proposed for a class of stochastic Takagi and Sugeno (T–S) fuzzy systems with mixed time delays to guarantee the optimal H2 and H∞ performance simultaneously. Firstly, based on the T–S fuzzy model, two form of nonfragile state feedback controllers are designed to stabilize the T–S fuzzy system, that is to say, nonfragile state feedback controllers minimize the H2 and H∞ performance simultaneously. Then, by applying T–S fuzzy approach, the multiobjective H2/H∞ nonfragile fuzzy control problem is transformed into linear matrix inequality (LMI)-constrained multiobjective problem (MOP). In addition, we efficiently solve Pareto optimal solutions for the MOP by employing LMI-based multiobjective evolution algorithm (MOEA). Finally, the validity of this approach is illustrated by a realistic design example
An Empirical Study of the Landscape of Open Source Projects in Baidu, Alibaba, and Tencent
Open source software has drawn more and more attention from researchers,
developers and companies nowadays. Meanwhile, many Chinese technology companies
are embracing open source and choosing to open source their projects.
Nevertheless, most previous studies are concentrated on international companies
such as Microsoft or Google, while the practical values of open source projects
of Chinese technology companies remain unclear. To address this issue, we
conduct a mixed-method study to investigate the landscape of projects open
sourced by three large Chinese technology companies, namely Baidu, Alibaba, and
Tencent (BAT). We study the categories and characteristics of open source
projects, the developer's perceptions towards open sourcing effort for these
companies, and the internationalization effort of their open source projects.
We collected 1,000 open source projects that were open sourced by BAT in GitHub
and performed an online survey that received 101 responses from developers of
these projects. Some key findings include: 1) BAT prefer to open source
frontend development projects, 2) 88\% of the respondents are positive towards
open sourcing software projects in their respective companies, 3) 64\% of the
respondents reveal that the most common motivations for BAT to open source
their projects are the desire to gain fame, expand their influence and gain
recruitment advantage, 4) respondents believe that the most common
internationalization effort is "providing an English version of readme files",
5) projects with more internationalization effort (i.e., include an English
readme file) are more popular. Our findings provide directions for software
engineering researchers and provide practical suggestions to software
developers and Chinese technology companies
Learning Second Order Local Anomaly for General Face Forgery Detection
In this work, we propose a novel method to improve the generalization ability
of CNN-based face forgery detectors. Our method considers the feature anomalies
of forged faces caused by the prevalent blending operations in face forgery
algorithms. Specifically, we propose a weakly supervised Second Order Local
Anomaly (SOLA) learning module to mine anomalies in local regions using deep
feature maps. SOLA first decomposes the neighborhood of local features by
different directions and distances and then calculates the first and second
order local anomaly maps which provide more general forgery traces for the
classifier. We also propose a Local Enhancement Module (LEM) to improve the
discrimination between local features of real and forged regions, so as to
ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial
Rich Model (ASRM) is introduced to help mine subtle noise features via
learnable high pass filters. With neither pixel level annotations nor external
synthetic data, our method using a simple ResNet18 backbone achieves
competitive performances compared with state-of-the-art works when evaluated on
unseen forgeries
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