Shrink & Cert: Bi-level Optimization for Certified Robustness
Nov 09, 2023
In this paper, we advance the concept of shrinking weights to train certifiably robust models from the fresh perspective of gradient-based bi-level optimization. Lack of robustness against adversarial attacks remains a challenge in safety-critical applications. Many attempts have been made in literature which only provide empirical verification of the defenses to certain attacks and can be easily broken. Methods in other lines of work can only develop certified guarantees of the model robustness in limited scenarios and are computationally expensive. We present a weight shrinkage formulation that is computationally inexpensive and can be solved as a simple first-order optimization problem. We show that model trained with our method has lower Lipschitz bounds in each layer, which directly provides formal guarantees on the certified robustness. We demonstrate that our approach, Shrink & Cert (SaC) achieves provably robust networks which simultaneously give excellent standard and robust accuracy. We demonstrate the success of our approach on CIFAR-10 and ImageNet datasets and compare them with existing robust training techniques. Code : https: //github.com/sagarverma/BiC
ICML Workshop 2023
Kavya Gupta , Sagar Verma
Post Wildfire Burnt-up Detection using Siamese UNet
In this article, we present an approach for detecting burnt area due to wild fire in Sentinel-2 images by leveraging the power of Siamese neural networks. By employing a Siamese network, we are able to efficiently encode the feature extraction process for pairs of images. This is achieved by utilizing two branches within the Siamese network, which capture and combine information at different resolutions to make predictions. The weights are shared between these two branches in siamese networks. This design allows to effectively analyze the changes between two remote sensing images, enabling precise identification of areas impacted by forest wildfires in the state of California as part of ChaBuD challenge thereby assisting local authorities in effectively monitoring the impacted regions and facilitating the restoration process. We experimented with various model architectures to train ChaBuD dataset and carefully evaluated the performance. Through rigorous testing and analysis, we have achieved promising results, ultimately obtaining a final private score (IoU) of 0.7495 on the hidden test dataset. The code is available at https://github.com/kavyagupta/chabud. We also deploy the final model as a point solution for anyone to use at https://firemap.io.
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics. In this paper, we present a novel deep learning framework for urban change detection which combines state-of-the-art fully convolutional networks (similar to U-Net) for feature representation and powerful recurrent networks (such as LSTMs) for temporal modeling. We report our results on the recently publicly available bi-temporal Onera Satellite Change Detection (OSCD) Sentinel-2 dataset, enhancing the temporal information with additional images of the same region on different dates. Moreover, we evaluate the performance of the recurrent networks as well as the use of the additional dates on the unseen test-set using an ensemble cross-validation strategy. All the developed models during the validation phase have scored an overall accuracy of more than 95%, while the use of LSTMs and further temporal information, boost the F1 rate of the change class by an additional 1.5%.