Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which have low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unmanned Aerial Vehicle(UAV) can effortlessly access difficult places during any disaster and collect high resolution imagery that is required for aforementioned tasks of computer vision. To address these issues we present a high resolution UAV imagery, FloodNet, captured after the hurricane Harvey. This dataset demonstrates the post flooded damages of the affected areas. The images are labeled pixel-wise for semantic segmentation task and questions are produced for the task of visual question answering. FloodNet poses several challenges including detection of flooded roads and buildings and distinguishing between natural water and flooded water. With the advancement of deep learning algorithms, we can analyze the impact of any disaster which can make a precise understanding of the affected areas. In this paper, we compare and contrast the performances of baseline methods for image classification, semantic segmentation, and visual question answering on our dataset.

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

Adversarial attacks that generate small Lp norm perturbations to mislead classifiers have limited success in black-box settings and with unseen classifiers. These attacks are also not robust to defenses that use denoising filters and to adversarial training procedures. Instead, adversarial attacks that generate unrestricted perturbations are more robust to defenses, are generally more successful in black-box settings and are more transferable to unseen classifiers. However, unrestricted perturbations may be noticeable to humans. In this paper, we propose a content-based black-box adversarial attack that generates unrestricted perturbations by exploiting image semantics to selectively modify colors within chosen ranges that are perceived as natural by humans. We show that the proposed approach, ColorFool, outperforms in terms of success rate, robustness to defense frameworks and transferability, five state-of-the-art adversarial attacks on two different tasks, scene and object classification, when attacking three state-of-the-art deep neural networks using three standard datasets. The source code is available at https://github.com/smartcameras/ColorFool.

/pdf/colorfool-semantic-adversarial-colorization-d0aulpsa5r.pdf

ColorFool: Semantic Adversarial Colorization

Flooding is the world’s most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure toward flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this letter, we propose several methods to perform flooding identification in high-resolution remote sensing images using deep learning. Specifically, some proposed techniques are based upon unique networks, such as dilated and deconvolutional ones, whereas others were conceived to exploit diversity of distinct networks in order to extract the maximum performance of each classifier. The evaluation of the proposed methods was conducted in a high-resolution remote sensing data set. Results show that the proposed algorithms outperformed the state-of-the-art baselines, providing improvements ranging from 1% to 4% in terms of the Jaccard Index.

/pdf/exploiting-convnet-diversity-for-flooding-identification-2u6uave17m.pdf

Exploiting ConvNet Diversity for Flooding Identification

. These last decades, Earth Observation brought a number of new perspectives from geosciences to human activity monitoring. As more data became available, Artificial Intelligence (AI) techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. To help the community go forward, we introduce a new dataset composed of co-registered optical and SAR images time series for the detection of flood events and new neural network approaches to leverage these two modalities.

/pdf/flood-detection-in-time-series-of-optical-and-sar-images-2hlzke1ubg.pdf

Flood detection in time series of optical and sar images

As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information from face photos, with the aid of, e.g., image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against face recognition systems. Our goal in this paper is to develop a technique that can encrypt the personal photos such that they can protect users from unauthorized face recognition systems but remain visually identical to the original version for human beings. To achieve this, we propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks which can be overlaid on facial images, such that the original identities can be concealed without sacrificing the visual quality. Extensive experiments demonstrate that TIP-IM provides 95\%+ protection success rate against various state-of-the-art face recognition models under practical test scenarios. Besides, we also show the practical and effective applicability of our method on a commercial API service.

Towards Face Encryption by Generating Adversarial Identity Masks.

/pdf/the-multimedia-satellite-task-at-mediaeval-2019-twy1t3zroi.pdf

The Multimedia Satellite Task at MediaEval 2019.

We introduce a new privacy task focused on images that users share online. The task benchmarks image transformation algorithms that are capable of blocking the ability of automatic classifiers to infer sensitive information in images. At the same time, the image transformations should maintain the original value of the image to the user who is sharing it, either by leaving it not obviously changed, or by enhancing it to increase its visual appeal. This year, the focus is on a set of 60 scene categories, selected from the Places365-Standard dataset, that can be considered privacy-sensitive.

/pdf/pixel-privacy-increasing-image-appeal-while-blocking-2j000auhsw.pdf

Pixel Privacy. Increasing Image Appeal while Blocking Automatic Inference of Sensitive Scene Information

The creation of realistic, synthetic datasets has several purposes with growing demand in recent times, e.g. privacy protection and other cases where real data cannot be easily shared. A multitude of primarily neural networks (NNs), e.g. Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), or Bayesian Network (BN) approaches have been created to tackle this problem, however these require extensive com-pute resources, lack interpretability, and in some instances lack replication ﬁdelity as well. We propose a hybrid, probabilistic approach for synthesizing pairwise independent tabular data, called Synthsonic. A sequence of well-understood, invertible statistical transformations removes ﬁrst-order correlations, then a Bayesian Network jointly models continuous and categorical variables, and a calibrated discriminative learner captures the remaining dependencies. Replication studies on MIT’s SDGym benchmark show marginally or signiﬁcantly better performance than all prior BN-based approaches, while being competitive with NN-based approaches (ﬁrst place in 10 out of 13 benchmark datasets). The computational time required to learn the data distribution is at least one order of magnitude lower than the NN methods. Furthermore, in-specting intermediate results during the synthetic data generation allows easy diagnostics and tailored corrections. We believe the combination of out-of-the-box performance, speed and interpretability make this method a signiﬁcant addition to the synthetic data generation toolbox.

Synthsonic: Fast, Probabilistic modeling and Synthesis of Tabular Data

The aim of the MediaEval 2018 Pixel Privacy task is to increase image appeal while blocking automatic inference of sensitive scene information. We investigate three different views from which we could consider enhancement: the view of the image aesthetics field, the view of automatic large-scale aesthetics inference models, and the view of social media users who reflect on their own photographic practices. Systematic image editing can do better than one-size-fits-all-filters with helping casual social media users find the desired photo look. Machine learning aesthetics assessment falls short when inferring individual preferences. A qualitative user study gives insight into the diversity and complexity of preferences.

Exploring Three Views on Image Enhancement for Pixel Privacy

/pdf/flood-severity-estimation-from-online-news-images-and-multi-gxu4usub75.pdf

Flood Severity Estimation from Online News Images and Multi-Temporal Satellite Images Using Deep Neural Networks.

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