77 research outputs found
Learning to Annotate Part Segmentation with Gradient Matching
The success of state-of-the-art deep neural networks heavily relies on the
presence of large-scale labelled datasets, which are extremely expensive and
time-consuming to annotate. This paper focuses on tackling semi-supervised part
segmentation tasks by generating high-quality images with a pre-trained GAN and
labelling the generated images with an automatic annotator. In particular, we
formulate the annotator learning as a learning-to-learn problem. Given a
pre-trained GAN, the annotator learns to label object parts in a set of
randomly generated images such that a part segmentation model trained on these
synthetic images with their predicted labels obtains low segmentation error on
a small validation set of manually labelled images. We further reduce this
nested-loop optimization problem to a simple gradient matching problem and
efficiently solve it with an iterative algorithm. We show that our method can
learn annotators from a broad range of labelled images including real images,
generated images, and even analytically rendered images. Our method is
evaluated with semi-supervised part segmentation tasks and significantly
outperforms other semi-supervised competitors when the amount of labelled
examples is extremely limited.Comment: ICLR 202
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
The robustness of animated text CAPTCHAs
PhD ThesisCAPTCHA is standard security technology that uses AI techniques to tells computer and
human apart. The most widely used CAPTCHA are text-based CAPTCHA schemes. The
robustness and usability of these CAPTCHAs relies mainly on the segmentation resistance
mechanism that provides robustness against individual character recognition attacks.
However, many CAPTCHAs have been shown to have critical flaws caused by many
exploitable invariants in their design, leaving only a few CAPTCHA schemes resistant to
attacks, including ReCAPTCHA and the Wikipedia CAPTCHA.
Therefore, new alternative approaches to add motion to the CAPTCHA are used to add
another dimension to the character cracking algorithms by animating the distorted
characters and the background, which are also supported by tracking resistance
mechanisms that prevent the attacks from identifying the main answer through frame-toframe
attacks. These technologies are used in many of the new CAPTCHA schemes
including the Yahoo CAPTCHA, CAPTCHANIM, KillBot CAPTCHAs, non-standard
CAPTCHA and NuCAPTCHA.
Our first question: can the animated techniques included in the new CAPTCHA schemes
provide the required level of robustness against the attacks? Our examination has shown
many of the CAPTCHA schemes that use the animated features can be broken through
tracking attacks including the CAPTCHA schemes that uses complicated tracking
resistance mechanisms.
The second question: can the segmentation resistance mechanism used in the latest standard
text-based CAPTCHA schemes still provide the additional required level of resistance
against attacks that are not present missed in animated schemes? Our test against the latest
version of ReCAPTCHA and the Wikipedia CAPTCHA exposed vulnerability problems
against the novel attacks mechanisms that achieved a high success rate against them.
The third question: how much space is available to design an animated text-based
CAPTCHA scheme that could provide a good balance between security and usability? We
designed a new animated text-based CAPTCHA using guidelines we designed based on the
results of our attacks on standard and animated text-based CAPTCHAs, and we then tested
its security and usability to answer this question.
ii
In this thesis, we put forward different approaches to examining the robustness of animated
text-based CAPTCHA schemes and other standard text-based CAPTCHA schemes against
segmentation and tracking attacks. Our attacks included several methodologies that
required thinking skills in order to distinguish the animated text from the other animated
noises, including the text distorted by highly tracking resistance mechanisms that displayed
them partially as animated segments and which looked similar to noises in other
CAPTCHA schemes. These attacks also include novel attack mechanisms and other
mechanisms that uses a recognition engine supported by attacking methods that exploit the
identified invariants to recognise the connected characters at once. Our attacks also
provided a guideline for animated text-based CAPTCHAs that could provide resistance to
tracking and segmentation attacks which we designed and tested in terms of security and
usability, as mentioned before. Our research also contributes towards providing a toolbox
for breaking CAPTCHAs in addition to a list of robustness and usability issues in the
current CAPTCHA design that can be used to provide a better understanding of how to
design a more resistant CAPTCHA scheme
A survey on generative adversarial networks for imbalance problems in computer vision tasks
Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms
- …