19 citations · 36 across the 18 of their papers we have counts for
9 papers · 1 filter
Prior-Conditioned Gaussian Discriminants for Generalizable AI-generated Image Detection
Shashank Kotyan, Makoto Shing, Yuki Imajuku +2
Diffusion-based generators have made synthetic images ubiquitous, but detectors often fail under simultaneous shifts in generator, prompt/style, and source-domain. We study AI-gene…
Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models
Shashank Kotyan, Pin-Yu Chen, Danilo Vasconcellos Vargas
Most evaluations of vision models use indirect methods to assess latent space quality. These methods often involve adding extra layers to project the latent space into a new one. T…
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!
Shashank Kotyan, Po-Yuan Mao, Pin-Yu Chen +1
Deep neural networks can be exploited using natural adversarial samples, which do not impact human perception. Current approaches often rely on deep neural networks' white-box natu…
Synthetic Shifts to Initial Seed Vector Exposes the Brittle Nature of Latent-Based Diffusion Models
Mao Po-Yuan, Shashank Kotyan, Tham Yik Foong +1
Recent advances in Conditional Diffusion Models have led to substantial capabilities in various domains. However, understanding the impact of variations in the initial seed vector…
The Challenges of Image Generation Models in Generating Multi-Component Images
Tham Yik Foong, Shashank Kotyan, Po Yuan Mao +1
Recent advances in text-to-image generators have led to substantial capabilities in image generation. However, the complexity of prompts acts as a bottleneck in the quality of imag…
Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning
Shashank Kotyan, Danilo Vasconcellos Vargas
Neural networks have revolutionized various domains, exhibiting remarkable accuracy in tasks like natural language processing and computer vision. However, their vulnerability to s…
Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation
Shashank Kotyan, Danilo Vasconcellos Vargas
Vision Transformer (ViT) has demonstrated promising performance in computer vision tasks, comparable to state-of-the-art neural networks. Yet, this new type of deep neural network…
Deep neural network loses attention to adversarial images
Shashank Kotyan, Danilo Vasconcellos Vargas
Adversarial algorithms have shown to be effective against neural networks for a variety of tasks. Some adversarial algorithms perturb all the pixels in the image minimally for the…
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences
Shashank Kotyan, Danilo Vasconcellos Vargas, Moe Matsuki
Neural networks have been shown vulnerable to a variety of adversarial algorithms. A crucial step to understanding the rationale for this lack of robustness is to assess the potent…