324 research outputs found
Stimulus Temporal Coherence Strongly Influences Rapid Plasticity in Primary Auditory Cortex under Global Attention
Temporal coherence of stimulus features is a key property of sounds that emanate from single source. Consequently, it is important to understand how it may influence the direction and extent of the rapid plasticity postulated to occur during the streaming of concurrent sounds. We postulated that when animals listen attentively to coherent or incoherent stimuli, responses would adapt to effectively encode the correlational structure of the stimuli. In this study, ferrets were trained to attend globally to two-tone sequences which were played either simultaneously (SYNC) or alternatively (ALT) on a trial-by-trial basis, and to detect a transition to a random cloud of tones by licking a waterspout for reward. Neuronal activities were collected in the primary auditory cortex during performing the task and passively listening to the same stimuli sequences. Compared with the passive condition, neuronal responses changed distinctively between SYNC and ALT trials under the effect of attention. These results provide neuronal evidence for the role of stimulus temporal coherence in modulating responses during attentive listening to complex sounds
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Deep neural networks are known to be vulnerable to adversarial examples
crafted by adding human-imperceptible perturbations to the benign input. After
achieving nearly 100% attack success rates in white-box setting, more focus is
shifted to black-box attacks, of which the transferability of adversarial
examples has gained significant attention. In either case, the common
gradient-based methods generally use the sign function to generate
perturbations on the gradient update, that offers a roughly correct direction
and has gained great success. But little work pays attention to its possible
limitation. In this work, we observe that the deviation between the original
gradient and the generated noise may lead to inaccurate gradient update
estimation and suboptimal solutions for adversarial transferability. To this
end, we propose a Sampling-based Fast Gradient Rescaling Method (S-FGRM).
Specifically, we use data rescaling to substitute the sign function without
extra computational cost. We further propose a Depth First Sampling method to
eliminate the fluctuation of rescaling and stabilize the gradient update. Our
method could be used in any gradient-based attacks and is extensible to be
integrated with various input transformation or ensemble methods to further
improve the adversarial transferability. Extensive experiments on the standard
ImageNet dataset show that our method could significantly boost the
transferability of gradient-based attacks and outperform the state-of-the-art
baselines.Comment: 10 pages, 6 figures, 7 tables. arXiv admin note: substantial text
overlap with arXiv:2204.0288
The Role of AM Symbiosis in Plant Adaptation to Drought Stress
Symposium paper Part 1: Function and management of soil microorganisms in agro-ecosystems with special reference to arbuscular mycorrhizal fung
FDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
The ability to create high-quality 3D faces from a single image has become
increasingly important with wide applications in video conferencing, AR/VR, and
advanced video editing in movie industries. In this paper, we propose Face
Diffusion NeRF (FDNeRF), a new generative method to reconstruct high-quality
Face NeRFs from single images, complete with semantic editing and relighting
capabilities. FDNeRF utilizes high-resolution 3D GAN inversion and expertly
trained 2D latent-diffusion model, allowing users to manipulate and construct
Face NeRFs in zero-shot learning without the need for explicit 3D data. With
carefully designed illumination and identity preserving loss, as well as
multi-modal pre-training, FD-NeRF offers users unparalleled control over the
editing process enabling them to create and edit face NeRFs using just
single-view images, text prompts, and explicit target lighting. The advanced
features of FDNeRF have been designed to produce more impressive results than
existing 2D editing approaches that rely on 2D segmentation maps for editable
attributes. Experiments show that our FDNeRF achieves exceptionally realistic
results and unprecedented flexibility in editing compared with state-of-the-art
3D face reconstruction and editing methods. Our code will be available at
https://github.com/BillyXYB/FDNeRF
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
Adversarial examples have been well known as a serious threat to deep neural
networks (DNNs). In this work, we study the detection of adversarial examples,
based on the assumption that the output and internal responses of one DNN model
for both adversarial and benign examples follow the generalized Gaussian
distribution (GGD), but with different parameters (i.e., shape factor, mean,
and variance). GGD is a general distribution family to cover many popular
distributions (e.g., Laplacian, Gaussian, or uniform). It is more likely to
approximate the intrinsic distributions of internal responses than any specific
distribution. Besides, since the shape factor is more robust to different
databases rather than the other two parameters, we propose to construct
discriminative features via the shape factor for adversarial detection,
employing the magnitude of Benford-Fourier coefficients (MBF), which can be
easily estimated using responses. Finally, a support vector machine is trained
as the adversarial detector through leveraging the MBF features. Extensive
experiments in terms of image classification demonstrate that the proposed
detector is much more effective and robust on detecting adversarial examples of
different crafting methods and different sources, compared to state-of-the-art
adversarial detection methods
- …