Paper 13

DAC-LoRA: Dynamic Adversarial Curriculum for Efficient and Robust Few-Shot Adaptation

A generalized framework that uses a dynamic, adversarial curriculum to make Vision-Language Models (VLMs) more robust against attacks, improving efficiency and few-shot adaptation

Paper 12

DINOHash: Learning Adversarially Robust Perceptual Hashes from Self-Supervised Features

An open-source framework for robust perceptual image hashing, DINOHash enables secure and transformation-resilient provenance detection of AI-generated images.

Paper 11

SPD Attack - Prevention of AI Powered Image Editing by Image Immunization

An analysis of methods to safeguard images against misuse in image-to-image editing models through reproduction and extension of existing research across various models and datasets.

Paper 10

From Teacher to Student: Tracking Memorization Through Model Distillation

An analysis of knowledge distillation effects on memorization in fine-tuned language models, showing that distillation from large teachers to smaller students mitigates memorization risks while improving efficiency.

Paper 9

Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models

In this study, we conduct a comprehensive reproducibility study of "An Image is Worth 1000 Lies: Adversarial Transferability Across Prompts on Vision-Language Models" validating the Cross-Prompt Attack (CroPA), and also proposing several key improvements to the framework.

Paper 8

[Re] CUDA: Curriculum of Data Augmentation for Long‐tailed Recognition

Using classwise degree of data augmentation to tackle class imbalance in long tailed dataset

Paper 7

Riemann Sum Optimization for Accurate Integrated Gradients Computation

A mathematical framework to reduce computational complexity of Integrated Gradients

Paper 6

A reproducability study of Important Direction Gradient Integration (IDGI)

Highlight key results or methods involved in 1-2 lines.

Paper 5

Rethinking Randomized Smoothing from the Perspective of Scalability

A study on randomized smoothing, analysed from the perspective of scalability as a challenge to its continued application

Paper 4

Image-Alchemy: Advancing Subject Fidelity in Personalized Text-to-Image Generation

A two-stage personalization pipeline for personalized image generation using LoRA-based attention fine-tuning and segmentation-guided Img2Img synthesis.

Paper 3

Detection Limits and Statistical Separability of Tree Ring Watermarks in Rectified Flow-based Text-to-Image Generation Models

Tree Ring Watermarks are harder to detect in modern rectified flow-based models compared to traditional diffusion models, especially under image attacks.

Paper 2

One Noise to Fool Them All: Universal Adversarial Defenses Against Image Editing

Image immunization involves adding undetectable noise in images to prevent editing via diffusion models. We further extended this to multiple images using a single noise.

Paper 1

Impact of Language Guidance: A Reproducibility Study

A reproducability study of Language guidance on self-supervised learning frameworks