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Found 6 papers, 1 papers with code
Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotation, and computational constraints remain.
Privacy Threats in Stable Diffusion Models
Our methodology involves observing the output of a stable diffusion model at different generative epochs and training a classification model to distinguish when a series of intermediates originated from a training sample or not.
Scale Invariant Privacy Preserving Video via Wavelet Decomposition
Video surveillance has become ubiquitous in the modern world.
Focused Adversarial Attacks
Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations.
Ulixes: Facial Recognition Privacy with Adversarial Machine Learning
In this paper we propose Ulixes, a strategy to generate visually non-invasive facial noise masks that yield adversarial examples, preventing the formation of identifiable user clusters in the embedding space of facial encoders.
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution
We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video.
