140 research outputs found
Shift Unitary Transform for Constructing Two-Dimensional Wavelet Filters
Due to the difficulty for constructing two-dimensional wavelet filters, the commonly used wavelet filters are tensor-product of one-dimensional wavelet filters. In some applications, more perfect reconstruction filters should be provided. In this paper, we introduce a transformation which is referred to as Shift Unitary Transform (SUT) of Conjugate Quadrature Filter (CQF). In terms of this transformation, we propose a parametrization method for constructing two-dimensional orthogonal wavelet filters. It is proved that tensor-product wavelet filters are only special cases of this parametrization method. To show this, we introduce the SUT of one-dimensional CQF and present a complete parametrization of one-dimensional wavelet system. As a result, more ways are provided to randomly generate two-dimensional perfect reconstruction filters
Coherence measures with respect to general quantum measurements
Quantum coherence with respect to orthonormal bases has been studied
extensively in the past few years. Recently, Bischof, et al. [Phys. Rev. Lett.
123, 110402 (2019)] generalized it to the case of general positive
operator-valued measure (POVM) measurements. Such POVM-based coherence,
including the block coherence as a special case, have significant operational
interpretations in quantifying the advantage of quantum states in quantum
information processing. In this work we first establish an alternative
framework for quantifying the block coherence and provide several block
coherence measures. We then present several coherence measures with respect to
POVM measurements, and prove a conjecture on the -norm related POVM
coherence measure.Comment: 11 pages, no figure
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
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
Effects of Chilling on the Structure, Function and Development of Chloroplasts
Chloroplasts are the organelles that perform energy transformation in plants. The normal physiological functions of chloroplasts are essential for plant growth and development. Chilling is a common environmental stress in nature that can directly affect the physiological functions of chloroplasts. First, chilling can change the lipid membrane state and enzyme activities in chloroplasts. Then, the efficiency of photosynthesis declines, and excess reactive oxygen species (ROS) are produced. On one hand, excess ROS can damage the chloroplast lipid membrane; on the other hand, ROS also represent a stress signal that can alter gene expression in both the chloroplast and nucleus to help regenerate damaged proteins, regulate lipid homeostasis, and promote plant adaptation to low temperatures. Furthermore, plants assume abnormal morphology, including chlorosis and growth retardation, with some even exhibiting severe necrosis under chilling stress. Here, we review the response of chloroplasts to low temperatures and focus on photosynthesis, redox regulation, lipid homeostasis, and chloroplast development to elucidate the processes involved in plant responses and adaptation to chilling stress
Pelvic floor MRI segmentation based on semi-supervised deep learning
The semantic segmentation of pelvic organs via MRI has important clinical
significance. Recently, deep learning-enabled semantic segmentation has
facilitated the three-dimensional geometric reconstruction of pelvic floor
organs, providing clinicians with accurate and intuitive diagnostic results.
However, the task of labeling pelvic floor MRI segmentation, typically
performed by clinicians, is labor-intensive and costly, leading to a scarcity
of labels. Insufficient segmentation labels limit the precise segmentation and
reconstruction of pelvic floor organs. To address these issues, we propose a
semi-supervised framework for pelvic organ segmentation. The implementation of
this framework comprises two stages. In the first stage, it performs
self-supervised pre-training using image restoration tasks. Subsequently,
fine-tuning of the self-supervised model is performed, using labeled data to
train the segmentation model. In the second stage, the self-supervised
segmentation model is used to generate pseudo labels for unlabeled data.
Ultimately, both labeled and unlabeled data are utilized in semi-supervised
training. Upon evaluation, our method significantly enhances the performance in
the semantic segmentation and geometric reconstruction of pelvic organs, Dice
coefficient can increase by 2.65% averagely. Especially for organs that are
difficult to segment, such as the uterus, the accuracy of semantic segmentation
can be improved by up to 3.70%
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