Search Results for author:
Found 13 papers, 3 papers with code
FLASH: Federated Learning Across Simultaneous Heterogeneities
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data.
Plug-and-Play Transformer Modules for Test-Time Adaptation
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model.
Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks
Our goal is to design an attack strategy that can learn from such natural scenes by leveraging the local patch differences that occur inherently in such images (e. g. difference between the local patch on the object `person' and the object `bike' in a traffic scene).
GAMA: Generative Adversarial Multi-Object Scene Attacks
Using the joint image-text features to train the generator, we show that GAMA can craft potent transferable perturbations in order to fool victim classifiers in various attack settings.
Zero-Query Transfer Attacks on Context-Aware Object Detectors
We compare our zero-query attack against a few-query scheme that repeatedly checks if the victim system is fooled.
Context-Aware Transfer Attacks for Object Detection
In this paper, we present a new approach to generate context-aware attacks for object detectors.
ADC: Adversarial attacks against object Detection that evade Context consistency checks
A very recent defense strategy for detecting adversarial examples, that has been shown to be robust to current attacks, is to check for intrinsic context consistencies in the input data, where context refers to various relationships (e. g., object-to-object co-occurrence relationships) in images.
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied.
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes
Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples.
Measurement-driven Security Analysis of Imperceptible Impersonation Attacks
The emergence of Internet of Things (IoT) brings about new security challenges at the intersection of cyber and physical spaces.
A4 : Evading Learning-based Adblockers
Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful.
IoTSan: Fortifying the Safety of IoT Systems
In this paper, we design IoTSan, a novel practical system that uses model checking as a building block to reveal "interaction-level" flaws by identifying events that can lead the system to unsafe states.
Cryptography and Security
Adversarial Perturbations Against Real-Time Video Classification Systems
We exploit recent advances in generative adversarial network (GAN) architectures to account for temporal correlations and generate adversarial samples that can cause misclassification rates of over 80% for targeted activities.
