7,902 research outputs found
Assessing Robustness via Score-Based Adversarial Image Generation
Most adversarial attacks and defenses focus on perturbations within small
-norm constraints. However, threat models cannot capture all
relevant semantic-preserving perturbations, and hence, the scope of robustness
evaluations is limited. In this work, we introduce Score-Based Adversarial
Generation (ScoreAG), a novel framework that leverages the advancements in
score-based generative models to generate adversarial examples beyond
-norm constraints, so-called unrestricted adversarial examples,
overcoming their limitations. Unlike traditional methods, ScoreAG maintains the
core semantics of images while generating realistic adversarial examples,
either by transforming existing images or synthesizing new ones entirely from
scratch. We further exploit the generative capability of ScoreAG to purify
images, empirically enhancing the robustness of classifiers. Our extensive
empirical evaluation demonstrates that ScoreAG matches the performance of
state-of-the-art attacks and defenses across multiple benchmarks. This work
highlights the importance of investigating adversarial examples bounded by
semantics rather than -norm constraints. ScoreAG represents an
important step towards more encompassing robustness assessments
Unsupervised Generative Adversarial Cross-modal Hashing
Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing unsupervised methods learn hashing functions by preserving inter and
intra correlations, while ignoring the underlying manifold structure across
different modalities, which is extremely helpful to capture meaningful nearest
neighbors of different modalities for cross-modal retrieval. To address the
above problem, in this paper we propose an Unsupervised Generative Adversarial
Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for
unsupervised representation learning to exploit the underlying manifold
structure of cross-modal data. The main contributions can be summarized as
follows: (1) We propose a generative adversarial network to model cross-modal
hashing in an unsupervised fashion. In the proposed UGACH, given a data of one
modality, the generative model tries to fit the distribution over the manifold
structure, and select informative data of another modality to challenge the
discriminative model. The discriminative model learns to distinguish the
generated data and the true positive data sampled from correlation graph to
achieve better retrieval accuracy. These two models are trained in an
adversarial way to improve each other and promote hashing function learning.
(2) We propose a correlation graph based approach to capture the underlying
manifold structure across different modalities, so that data of different
modalities but within the same manifold can have smaller Hamming distance and
promote retrieval accuracy. Extensive experiments compared with 6
state-of-the-art methods verify the effectiveness of our proposed approach.Comment: 8 pages, accepted by 32th AAAI Conference on Artificial Intelligence
(AAAI), 201
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