309 research outputs found
A New Two-dimensional Model-based Subspace Method for Large-scale Unconstrained Derivative-free Optimization: 2D-MoSub
This paper proposes the method 2D-MoSub (2-dimensional model-based subspace
method), which is a novel derivative-free optimization (DFO) method based on
the subspace method for general unconstrained optimization and especially aims
to solve large-scale DFO problems. 2D-MoSub combines 2-dimensional quadratic
interpolation models and trust-region techniques to iteratively update the
points and explore the 2-dimensional subspace. 2D-MoSub's framework includes
initialization, constructing the interpolation set, building the quadratic
interpolation model, performing trust-region trial steps, and updating the
trust-region radius and subspace. Experimental results demonstrate the
effectiveness and efficiency of 2D-MoSub in solving a variety of optimization
problems.Comment: 22 page
Derivative-Free Optimization with Transformed Objective Functions (DFOTO) and the Algorithm Based on the Least Frobenius Norm Updating Quadratic Model
Derivative-free optimization problems are optimization problems where
derivative information is unavailable. The least Frobenius norm updating
quadratic interpolation model function is one of the essential under-determined
model functions for model-based derivative-free trust-region methods. This
article proposes derivative-free optimization with transformed objective
functions and gives a trust-region method with the least Frobenius norm model.
The model updating formula is based on Powell's formula. The method shares the
same framework with those for problems without transformations, and its query
scheme is given. We propose the definitions related to optimality-preserving
transformations to understand the interpolation model in our method. We prove
the existence of model optimality-preserving transformations beyond translation
transformation. The necessary and sufficient condition for such transformations
is given. The affine transformation with a positive multiplication coefficient
is not model optimality-preserving. We also analyze the corresponding least
Frobenius norm updating model and its interpolation error when the objective
function is affinely transformed. Convergence property of a provable
algorithmic framework containing our model is given. Numerical results of
solving test problems and a real-world problem with the implementation
NEWUOA-Trans show that our method can successfully solve most problems with
objective optimality-preserving transformations, even though such
transformations will change the optimality of the model function. To our best
knowledge, this is the first work providing the model-based derivative-free
algorithm and analysis for transformed problems with the function evaluation
oracle (not the function-value comparison oracle). This article also proposes
the "moving-target" optimization problem.Comment: 42 pages, Derivative-Free Optimization with Transformed Objective
Functions (DFOTO) and the Algorithm Based on the Least Frobenius Norm
Updating Quadratic Mode
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
The majority of point cloud registration methods currently rely on extracting
features from points. However, these methods are limited by their dependence on
information obtained from a single modality of points, which can result in
deficiencies such as inadequate perception of global features and a lack of
texture information. Actually, humans can employ visual information learned
from 2D images to comprehend the 3D world. Based on this fact, we present a
novel Cross-Modal Information-Guided Network (CMIGNet), which obtains global
shape perception through cross-modal information to achieve precise and robust
point cloud registration. Specifically, we first incorporate the projected
images from the point clouds and fuse the cross-modal features using the
attention mechanism. Furthermore, we employ two contrastive learning
strategies, namely overlapping contrastive learning and cross-modal contrastive
learning. The former focuses on features in overlapping regions, while the
latter emphasizes the correspondences between 2D and 3D features. Finally, we
propose a mask prediction module to identify keypoints in the point clouds.
Extensive experiments on several benchmark datasets demonstrate that our
network achieves superior registration performance.Comment: 8 pages, accepted by RAL 202
Timbre-reserved Adversarial Attack in Speaker Identification
As a type of biometric identification, a speaker identification (SID) system
is confronted with various kinds of attacks. The spoofing attacks typically
imitate the timbre of the target speakers, while the adversarial attacks
confuse the SID system by adding a well-designed adversarial perturbation to an
arbitrary speech. Although the spoofing attack copies a similar timbre as the
victim, it does not exploit the vulnerability of the SID model and may not make
the SID system give the attacker's desired decision. As for the adversarial
attack, despite the SID system can be led to a designated decision, it cannot
meet the specified text or speaker timbre requirements for the specific attack
scenarios. In this study, to make the attack in SID not only leverage the
vulnerability of the SID model but also reserve the timbre of the target
speaker, we propose a timbre-reserved adversarial attack in the speaker
identification. We generate the timbre-reserved adversarial audios by adding an
adversarial constraint during the different training stages of the voice
conversion (VC) model. Specifically, the adversarial constraint is using the
target speaker label to optimize the adversarial perturbation added to the VC
model representations and is implemented by a speaker classifier joining in the
VC model training. The adversarial constraint can help to control the VC model
to generate the speaker-wised audio. Eventually, the inference of the VC model
is the ideal adversarial fake audio, which is timbre-reserved and can fool the
SID system.Comment: 11 pages, 8 figure
Pseudo-Siamese Network based Timbre-reserved Black-box Adversarial Attack in Speaker Identification
In this study, we propose a timbre-reserved adversarial attack approach for
speaker identification (SID) to not only exploit the weakness of the SID model
but also preserve the timbre of the target speaker in a black-box attack
setting. Particularly, we generate timbre-reserved fake audio by adding an
adversarial constraint during the training of the voice conversion model. Then,
we leverage a pseudo-Siamese network architecture to learn from the black-box
SID model constraining both intrinsic similarity and structural similarity
simultaneously. The intrinsic similarity loss is to learn an intrinsic
invariance, while the structural similarity loss is to ensure that the
substitute SID model shares a similar decision boundary to the fixed black-box
SID model. The substitute model can be used as a proxy to generate
timbre-reserved fake audio for attacking. Experimental results on the Audio
Deepfake Detection (ADD) challenge dataset indicate that the attack success
rate of our proposed approach yields up to 60.58% and 55.38% in the white-box
and black-box scenarios, respectively, and can deceive both human beings and
machines.Comment: 5 page
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