6 citations · 14 across the 8 of their papers we have counts for
8 papers
On Transfer-based Universal Attacks in Pure Black-box Setting
Mohammad A. A. K. Jalwana, Naveed Akhtar, Ajmal Mian +2
Despite their impressive performance, deep visual models are susceptible to transferable black-box adversarial attacks. Principally, these attacks craft perturbations in a target m…
Rethinking interpretation: Input-agnostic saliency mapping of deep visual classifiers
Naveed Akhtar, Mohammad A. A. K. Jalwana
Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby…
CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency
Mohammad A. A. K. Jalwana, Naveed Akhtar, Mohammed Bennamoun +1
Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input. However, class-insensitivity of the ea…
Attack to Fool and Explain Deep Networks
Naveed Akhtar, Muhammad A. A. K. Jalwana, Mohammed Bennamoun +1
Deep visual models are susceptible to adversarial perturbations to inputs. Although these signals are carefully crafted, they still appear noise-like patterns to humans. This obser…
Orthogonal Deep Models As Defense Against Black-Box Attacks
Mohammad A. A. K. Jalwana, Naveed Akhtar, Mohammed Bennamoun +1
Deep learning has demonstrated state-of-the-art performance for a variety of challenging computer vision tasks. On one hand, this has enabled deep visual models to pave the way for…
Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud
Uzair Nadeem, Mohammad A. A. K. Jalwana, Mohammed Bennamoun +2
We propose a novel concept to directly match feature descriptors extracted from RGB images, with feature descriptors extracted from 3D point clouds. We use this concept to localize…
Label Universal Targeted Attack
Naveed Akhtar, Mohammad A. A. K. Jalwana, Mohammed Bennamoun +1
We introduce Label Universal Targeted Attack (LUTA) that makes a deep model predict a label of attacker's choice for `any' sample of a given source class with high probability. Our…
Improving Image-Based Localization with Deep Learning: The Impact of the Loss Function
Isaac Ronald Ward, M. A. Asim K. Jalwana, Mohammed Bennamoun
This work investigates the impact of the loss function on the performance of Neural Networks, in the context of a monocular, RGB-only, image localization task. A common technique u…