TL;DR
This work suggests that existing AI-generated image detectors are easily deceived by adversarial perturbations, highlighting the need for more robust detectors.
This work proposes two novel attack methods targeted at detectors that leverage autoencoder reconstruction error and finds that by constructing imperceptible adversarial examples, the distance between original and reconstruction can be artificially increased, causing fake images to be wrongly classified as real.
Deep Neural Networks (DNNs) remain vulnerable to adversarial perturbations, raising significant concerns in image processing applications, particularly in high-stakes domains such as medical imaging and security-critical systems. Most existing defense strategies are limited by domain specificity, architectural dependen...
Reliable traffic-object detection remains challenging when visual conditions degrade or when the detector is exposed to adversarial perturbations. This study investigates these robustness challenges through a systematic evaluation of lightweight YOLO detectors for intelligent traffic monitoring. Several nano-scale vari...
The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for adversaries targeting downstream tasks. To ensure trustworthiness of critical imaging pipelines, we must be able to detect adversarial behavior within preprocessing model...
This study seeks to discern whether images featuring avatars can be effectively used to generate adversarial examples capable of fully masking a person, and establishes a method of utilizing virtual avatars to provide increased privacy for users.
This work introduces \methodname{}, an adversarial reinforcement learning framework that pits two heterogeneous models against each other, and shows how a detector trained within this loop improves monotonically across rounds on each of three external benchmarks.
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