Search Results for author:
Found 23 papers, 16 papers with code
Towards Backdoor Stealthiness in Model Parameter Space
To answer this question, we examine 12 common backdoor attacks that focus on input-space or feature-space stealthiness and 17 diverse representative defenses.
RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agents
To facilitate research and application in this area, we present a new dataset named RescueADI, which contains high-resolution RSIs with annotations for three connected aspects: planning, perception, and recognition.
TopoNAS: Boosting Search Efficiency of Gradient-based NAS via Topological Simplification
Improving search efficiency serves as one of the crucial objectives of Neural Architecture Search (NAS).
Continual Panoptic Perception: Towards Multi-modal Incremental Interpretation of Remote Sensing Images
In this paper, we propose Continual Panoptic Perception (CPP), a unified continual learning model that leverages multi-task joint learning covering pixel-level classification, instance-level segmentation and image-level perception for universal interpretation in remote sensing images.
The infrastructure powering IBM's Gen AI model development
This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks.
BAN: Detecting Backdoors Activated by Adversarial Neuron Noise
Backdoor attacks on deep learning represent a recent threat that has gained significant attention in the research community.
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation
Experimental results on FineGrip demonstrate the feasibility of the panoptic perception task and the beneficial effect of multi-task joint optimization on individual tasks.
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
Vision Transformers (ViTs) achieve excellent performance in various tasks, but they are also vulnerable to adversarial attacks.
Beyond Neural-on-Neural Approaches to Speaker Gender Protection
Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i. e., perturbations are generated and tested with a neural network.
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression
Perturbative availability poisons (PAPs) add small changes to images to prevent their use for model training.
Generative Poisoning Using Random Discriminators
We introduce ShortcutGen, a new data poisoning attack that generates sample-dependent, error-minimizing perturbations by learning a generator.
Monolith: Real Time Recommendation System With Collisionless Embedding Table
In this paper, we present Monolith, a system tailored for online training.
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection
The primary approach to identifying vulnerable regions involves investigating realizable AEs, but generating these feasible apps poses a challenge.
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples
Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i. e. images whose content cannot be used to improve a classifier during training.
On Success and Simplicity: A Second Look at Transferable Targeted Attacks
In particular, we, for the first time, identify that a simple logit loss can yield competitive results with the state of the arts.
Adversarial Image Color Transformations in Explicit Color Filter Space
In particular, our color filter space is explicitly specified so that we are able to provide a systematic analysis of model robustness against adversarial color transformations, from both the attack and defense perspectives.
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start
Our experiments evaluate the danger of these attacks when mounted against three representative visually-aware recommender algorithms in a framework that uses images to address cold start.
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter
We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification.
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance
The success of image perturbations that are designed to fool image classifier is assessed in terms of both adversarial effect and visual imperceptibility.
Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification
The paper presents Columbia team{'}s participation in the SemEval 2019 Shared Task 7: RumourEval 2019.
Non-Determinism in Neural Networks for Adversarial Robustness
Recent breakthroughs in the field of deep learning have led to advancements in a broad spectrum of tasks in computer vision, audio processing, natural language processing and other areas.
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval
An adversarial query is an image that has been modified to disrupt content-based image retrieval (CBIR) while appearing nearly untouched to the human eye.
Exploiting Unlabeled Data for Neural Grammatical Error Detection
Identifying and correcting grammatical errors in the text written by non-native writers has received increasing attention in recent years.
