scholarly journals What do we see behind an occluder? Amodal completion of statistical properties in complex objects

2021 ◽  
Author(s):  
Thomas Cherian ◽  
SP Arun
Keyword(s):  
Neural Networks ◽  
Visual Search ◽  
Real World ◽  
Deep Neural Networks ◽  
Statistical Properties ◽  
Amodal Completion ◽  
Complex Objects ◽  
Similarity Relations ◽  
Complex Features

When a spiky object is occluded, we have the compelling percept that its spiky features continue behind the occluder. Although many real-world objects contain complex features, it is unclear how their features are amodally completed and whether this process is automatic. To this end, we asked participants to search for oddball targets among distractors and asked whether similarity relations in visual search between occluded displays match better with global or local completions of these displays. In Experiment 1, when objects with curved/straight corners were occluded, they were perceived as continuing with the same features than with them exchanged. In Experiment 2, we obtained similar results for objects with irregular/symmetric features. Analogous investigations on deep neural networks revealed similar results for curved/straight contours but not for irregular/symmetric features. Thus, amodal completion involves extending not only simple contours, but also their more global statistical properties.

Chapter 15. Human-Centered Concept Explanations for Neural Networks

2021 ◽  
Author(s):  
Chih-Kuan Yeh ◽  
Been Kim ◽  
Pradeep Ravikumar
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Case Studies ◽  
Real World ◽  
Deep Neural Networks ◽  
Learning Models ◽  
Real World Applications ◽  
The Right ◽  
Concept Activation ◽  
Machine Learning Models

Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective, and may not necessarily effectively communicate why the model is making its predictions at the right level of abstraction. For example, providing importance weights to individual pixels in an image can only express which parts of that particular image is important to the model, but humans may prefer an explanation which explains the prediction by concept-based thinking. In this work, we review the emerging area of concept based explanations. We start by introducing concept explanations including the class of Concept Activation Vectors (CAV) which characterize concepts using vectors in appropriate spaces of neural activations, and discuss different properties of useful concepts, and approaches to measure the usefulness of concept vectors. We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.

scholarly journals Transfer Learning and Deep Domain Adaptation

2020 ◽  
Author(s):  
Wen Xu ◽  
Jing He ◽  
Yanfeng Shu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Transfer Learning ◽  
Real World ◽  
Deep Neural Networks ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Real World Applications ◽  
Comprehensive Survey ◽  
Sample Reconstruction

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.

scholarly journals Really natural adversarial examples

2021 ◽  
Author(s):  
Anibal Pedraza ◽  
Oscar Deniz ◽  
Gloria Bueno
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Recognition ◽  
Image Quality ◽  
Real World ◽  
Deep Neural Networks ◽  
Distance Metrics ◽  
The Real ◽  
Adversarial Examples ◽  
General Object

AbstractThe phenomenon of Adversarial Examples has become one of the most intriguing topics associated to deep learning. The so-called adversarial attacks have the ability to fool deep neural networks with inappreciable perturbations. While the effect is striking, it has been suggested that such carefully selected injected noise does not necessarily appear in real-world scenarios. In contrast to this, some authors have looked for ways to generate adversarial noise in physical scenarios (traffic signs, shirts, etc.), thus showing that attackers can indeed fool the networks. In this paper we go beyond that and show that adversarial examples also appear in the real-world without any attacker or maliciously selected noise involved. We show this by using images from tasks related to microscopy and also general object recognition with the well-known ImageNet dataset. A comparison between these natural and the artificially generated adversarial examples is performed using distance metrics and image quality metrics. We also show that the natural adversarial examples are in fact at a higher distance from the originals that in the case of artificially generated adversarial examples.

DeepRepair: Style-Guided Repairing for Deep Neural Networks in the Real-World Operational Environment

2021 ◽  
pp. 1-16
Author(s):  
Bing Yu ◽  
Hua Qi ◽  
Guo Qing ◽  
Felix Juefei-Xu ◽  
Xiaofei Xie ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Deep Neural Networks ◽  
Operational Environment ◽  
The Real

scholarly journals Exposing Vulnerabilities of Deepfake Detection Systems with Robust Attacks

2021 ◽  
Author(s):  
Shehzeen Hussain ◽  
Paarth Neekhara ◽  
Brian Dolhansky ◽  
Joanna Bitton ◽  
Cristian Canton Ferrer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Video Compression ◽  
Real World ◽  
Deep Neural Networks ◽  
Black Box ◽  
Detection Methods ◽  
Detection Systems ◽  
Attack Scenario ◽  
Video Manipulation ◽  
Image And Video Compression

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on Deep Neural Networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN based Deepfake detectors into classifying fake videos as real. Finally, we study the extent to which adversarial perturbations transfer across different Deepfake detectors and create more accessible attacks using universal adversarial perturbations that pose a very feasible attack scenario since they can be easily shared amongst attackers.

scholarly journals Structured Probabilistic End-to-End Learning from Crowds

2020 ◽  
Author(s):  
Zhijun Chen ◽  
Huimin Wang ◽  
Hailong Sun ◽  
Pengpeng Chen ◽  
Tao Han ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Probabilistic Model ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Probabilistic Approach ◽  
Probabilistic Interpretation ◽  
End To End ◽  
Real World Datasets ◽  
The Relationship

End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art.

scholarly journals Adversarial Examples in Physical World

2021 ◽  
Author(s):  
Jiakai Wang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Potential Risk ◽  
Real World ◽  
Deep Neural Networks ◽  
Physical World ◽  
The Real ◽  
Wide Range ◽  
Adversarial Examples ◽  
Ai Safety

Although deep neural networks (DNNs) have already made fairly high achievements and a very wide range of impact, their vulnerability attracts lots of interest of researchers towards related studies about artificial intelligence (AI) safety and robustness this year. A series of works reveals that the current DNNs are always misled by elaborately designed adversarial examples. And unfortunately, this peculiarity also affects real-world AI applications and places them at potential risk. we are more interested in physical attacks due to their implementability in the real world. The study of physical attacks can effectively promote the application of AI techniques, which is of great significance to the security development of AI.

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