Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models

Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a patient and translates it to a new image that indicates an attacker-chosen disease condition. The potential for fraudulent claims based on such generated 'fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities. We show that these attacks manage to mislead both medical professionals and algorithmic detection schemes. Lastly, we also investigate defensive measures based on machine learning to detect images generated by Jekyll.Comment: Published in proceedings of the 5th European Symposium on Security and Privacy (EuroS&P '20

Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies

Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate

A coupled ground heat flux-surface energy balance model of evaporation using thermal remote sensing observations

One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG

A baseline estimate of regional agricultural water demand from GEO-LEO satellite observations

Agricultural water demand (AWD) and irrigation water demand (IWD) were assessed (2009–2018) over India using geostationary and polar orbiting satellites. A novel concept of satellite based composite crop-coefficient was introduced to address bulk AWD from mixed agricultural landscape. Significant spatio-temporal variation of AWD was observed over India. The decadal mean of annual AWD was found to be 1521 km3 contributing around 52% (789 km3) and 48% (732 km3) in kharif and rabi seasons, respectively. The decadal average IWD over India was found to be 360 km3. At annual scale, around 75% of AWD was found fulfilled by effective rainfall and the rest 25% is the IWD. The decadal trend of AWD and IWD showed significant increasing trend over Indian region. The study provides a baseline reference for regional agricultural water management policy over diverse agro-climatic regions of India with an opportunity to optimize AWD and IWD at different locations

Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data

Periodic crop condition monitoring is of prime importance in cotton belt of western India for water stress management. In this article, vegetation water content (VWC) is assessed using Radar Vegetation Index (RVI) derived from the RISAT-1 data during July to September, vegetative to first picking phase, for utilizing its potential for large area cotton condition assessment. The RVI estimation from dual-polarized data has been demonstrated for regional applications. Prediction models of VWC for cotton crop using RVI and in situ ground measurements depicts significant relationship, with R2 varying from 0.5 to 0.6 and RMSE of 0.3–0.7 kg m−2. High correlation exists between RVI with crop age and crop biomass with R2 varying from 0.55 to 0.7, this proves useful for sowing date prediction. The results showed good validation (R2 = 0.8) for operational applications. The estimated VWC was found with 30–35% error above 4 kg m−2 biomasses as compared to 20–25% in lower ranges

Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India

Estimation of latent heat flux at the agricultural field scale is required for proper water management. The current generation thermal sensors except Landsat-8 provide data on the order of 1000 m. The aim of this study is to test three approaches based on contextual models using only remote sensing datasets for the disaggregation of latent heat flux over India. The first two approaches are, respectively, based on the estimation of the evaporative fraction (EF) and solar radiation ratio at coarser resolution and disaggregating them to yield the latent heat flux at a finer resolution. The third approach is based on disaggregation of the thermal data and estimating a finer resolution latent heat flux. The three approaches were tested using MODIS datasets and the validation was done using the Bowen Ratio energy balance observations at five sites across India. From the validation, it was observed that the first two approaches performed similarly and better than the third approach at all five sites. The third approach, based on the disaggregation of the thermal data, yielded larger errors. In addition to better performance, the second approach based on the disaggregation of solar radiation ratio was simpler and required lesser data processing than the other approaches. In addition, the first two approaches captured the spatial pattern of latent heat flux without introducing any artefacts in the final output

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Evaluation of modified Dubois model for estimating surface soil moisture using dual polarization RISAT-1 C-band SAR data

The present study focuses on the soil moisture estimation using dual polarimetric RISAT-1, C-band SAR data in HH and HV polarization. The semi empirical approach derived by Dubois and the same modified and proposed as modified Dubois model (MDM) is worked for winter wheat crop from initial vegetative to maturity stage. Surface roughness model derived roughness is replaced with ‘s’ parameter for retrieval of dielectric constant which is further used in Topp’s model to derive soil moisture. Model derived soil moisture and ground measured soil moisture demonstrates pragmatic correlation after performing linear regression. The behaviour of inversion model varies at different crop stages depending on the soil exposure and crop cover. Normalized difference vegetation index from moderate resolution imaging spectroradiometer has also been evaluated to monitor crop status. Performance of MDM is promising for early and maturity stages of crop for soil moisture estimation