Convolutional Neural Networks (CNNs) have brought revolutionary advances to
many research areas due to their capacity of learning from raw data. However,
when those methods are applied to non-controllable environments, many different
factors can degrade the model's expected performance, such as unlabeled
datasets with different levels of domain shift and category shift.
Particularly, when both issues occur at the same time, we tackle this
challenging setup as Open Set Domain Adaptation (OSDA) problem. In general,
existing OSDA approaches focus their efforts only on aligning known classes or,
if they already extract possible negative instances, use them as a new category
learned with supervision during the course of training. We propose a novel way
to improve OSDA approaches by extracting a high-confidence set of unknown
instances and using it as a hard constraint to tighten the classification
boundaries of OSDA methods. Especially, we adopt a new loss constraint
evaluated in three different means, (1) directly with the pristine negative
instances; (2) with randomly transformed negatives using data augmentation
techniques; and (3) with synthetically generated negatives containing
adversarial features. We assessed all approaches in an extensive set of
experiments based on OVANet, where we could observe consistent improvements for
two public benchmarks, the Office-31 and Office-Home datasets, yielding
absolute gains of up to 1.3% for both Accuracy and H-Score on Office-31 and
5.8% for Accuracy and 4.7% for H-Score on Office-Home.

通过提取未知实例的高置信度集合，并将其用作限制条件来收紧开放集域自适应方法的分类边界，改进了开放集域自适应方法。在包含 Office-31 和 Office-Home 数据集的广泛实验中，对 OVANet 进行评估，观察到连续改进的结果，Office-31 的准确率和 H-Score 绝对增益高达 1.3％，Office-Home 的准确率增益为 5.8％，H-Score 增益为 4.7％。

通过未知利用在开放集领域适应中收紧分类边界

Tightening Classification Boundaries in Open Set Domain Adaptation  through Unknown Exploitation

Face recognition service providers protect face privacy by extracting compact
and discriminative facial features (representations) from images, and storing
the facial features for real-time recognition. However, such features can still
be exploited to recover the appearance of the original face by building a
reconstruction network. Although several privacy-preserving methods have been
proposed, the enhancement of face privacy protection is at the expense of
accuracy degradation. In this paper, we propose an adversarial features-based
face privacy protection (AdvFace) approach to generate privacy-preserving
adversarial features, which can disrupt the mapping from adversarial features
to facial images to defend against reconstruction attacks. To this end, we
design a shadow model which simulates the attackers' behavior to capture the
mapping function from facial features to images and generate adversarial latent
noise to disrupt the mapping. The adversarial features rather than the original
features are stored in the server's database to prevent leaked features from
exposing facial information. Moreover, the AdvFace requires no changes to the
face recognition network and can be implemented as a privacy-enhancing plugin
in deployed face recognition systems. Extensive experimental results
demonstrate that AdvFace outperforms the state-of-the-art face
privacy-preserving methods in defending against reconstruction attacks while
maintaining face recognition accuracy.

本研究提出了一种基于对抗特征的面部隐私保护 (AdvFace) 方法，通过在对手模型中生成对抗性的潜在噪声来破坏从对抗性特征到面部图像的映射，生成隐私保护的对抗性特征来防御面部重建攻击。实验表明，AdvFace 在维护面部识别准确性的同时，比目前最先进的面部隐私保护方法更有效地防御重建攻击。