27 research outputs found
AtLoc: Attention Guided Camera Localization
Deep learning has achieved impressive results in camera localization, but
current single-image techniques typically suffer from a lack of robustness,
leading to large outliers. To some extent, this has been tackled by sequential
(multi-images) or geometry constraint approaches, which can learn to reject
dynamic objects and illumination conditions to achieve better performance. In
this work, we show that attention can be used to force the network to focus on
more geometrically robust objects and features, achieving state-of-the-art
performance in common benchmark, even if using only a single image as input.
Extensive experimental evidence is provided through public indoor and outdoor
datasets. Through visualization of the saliency maps, we demonstrate how the
network learns to reject dynamic objects, yielding superior global camera pose
regression performance. The source code is avaliable at
https://github.com/BingCS/AtLoc
ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. The dataset is built by first optimizing a set of adversarial patches against an ensemble of models, using a state-of-the-art attack that creates transferable patches. The corresponding patches are then randomly rotated and translated, and finally applied to the ImageNet data. We use ImageNet-Patch to benchmark the robustness of 127 models against patch attacks, and also validate the effectiveness of the given patches in the physical domain (i.e., by printing and applying them to real-world objects). We conclude by discussing how our dataset could be used as a benchmark for robustness, and how our methodology can be generalized to other domains. We open source our dataset and evaluation code at https://github.com/pralab/ImageNet-Patch
Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images
Advanced Patch Attacks (PAs) on object detection in natural images have
pointed out the great safety vulnerability in methods based on deep neural
networks. However, little attention has been paid to this topic in Optical
Remote Sensing Images (O-RSIs). To this end, we focus on this research, i.e.,
PAs on object detection in O-RSIs, and propose a more Threatening PA without
the scarification of the visual quality, dubbed TPA. Specifically, to address
the problem of inconsistency between local and global landscapes in existing
patch selection schemes, we propose leveraging the First-Order Difference (FOD)
of the objective function before and after masking to select the sub-patches to
be attacked. Further, considering the problem of gradient inundation when
applying existing coordinate-based loss to PAs directly, we design an IoU-based
objective function specific for PAs, dubbed Bounding box Drifting Loss (BDL),
which pushes the detected bounding boxes far from the initial ones until there
are no intersections between them. Finally, on two widely used benchmarks,
i.e., DIOR and DOTA, comprehensive evaluations of our TPA with four typical
detectors (Faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable
effectiveness. To the best of our knowledge, this is the first attempt to study
the PAs on object detection in O-RSIs, and we hope this work can get our
readers interested in studying this topic