33,050 research outputs found
Audio Deepfake Detection: A Survey
Audio deepfake detection is an emerging active topic. A growing number of
literatures have aimed to study deepfake detection algorithms and achieved
effective performance, the problem of which is far from being solved. Although
there are some review literatures, there has been no comprehensive survey that
provides researchers with a systematic overview of these developments with a
unified evaluation. Accordingly, in this survey paper, we first highlight the
key differences across various types of deepfake audio, then outline and
analyse competitions, datasets, features, classifications, and evaluation of
state-of-the-art approaches. For each aspect, the basic techniques, advanced
developments and major challenges are discussed. In addition, we perform a
unified comparison of representative features and classifiers on ASVspoof 2021,
ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively.
The survey shows that future research should address the lack of large scale
datasets in the wild, poor generalization of existing detection methods to
unknown fake attacks, as well as interpretability of detection results
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization
Recommender systems leverage user demographic information, such as age,
gender, etc., to personalize recommendations and better place their targeted
ads. Oftentimes, users do not volunteer this information due to privacy
concerns, or due to a lack of initiative in filling out their online profiles.
We illustrate a new threat in which a recommender learns private attributes of
users who do not voluntarily disclose them. We design both passive and active
attacks that solicit ratings for strategically selected items, and could thus
be used by a recommender system to pursue this hidden agenda. Our methods are
based on a novel usage of Bayesian matrix factorization in an active learning
setting. Evaluations on multiple datasets illustrate that such attacks are
indeed feasible and use significantly fewer rated items than static inference
methods. Importantly, they succeed without sacrificing the quality of
recommendations to users.Comment: This is the extended version of a paper that appeared in ACM RecSys
201
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
Many different machine learning algorithms exist; taking into account each
algorithm's hyperparameters, there is a staggeringly large number of possible
alternatives overall. We consider the problem of simultaneously selecting a
learning algorithm and setting its hyperparameters, going beyond previous work
that addresses these issues in isolation. We show that this problem can be
addressed by a fully automated approach, leveraging recent innovations in
Bayesian optimization. Specifically, we consider a wide range of feature
selection techniques (combining 3 search and 8 evaluator methods) and all
classification approaches implemented in WEKA, spanning 2 ensemble methods, 10
meta-methods, 27 base classifiers, and hyperparameter settings for each
classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup
09, variants of the MNIST dataset and CIFAR-10, we show classification
performance often much better than using standard selection/hyperparameter
optimization methods. We hope that our approach will help non-expert users to
more effectively identify machine learning algorithms and hyperparameter
settings appropriate to their applications, and hence to achieve improved
performance.Comment: 9 pages, 3 figure
- …