8 accepted papers
Even after decades of advances in neural network training, the inherent robustness challenge remains open. While the sensitivity to adversarial perturbations is understandable given their intentional learning, the most surprising fact is the vulnerability to natural corruptions. Moreover, the reason…
Government verification systems are increasingly relying on internet-based platforms, where users authenticate their identities by uploading images captured with ordinary mobile devices. However, the rapid advancements in generative algorithms have enabled the creation of highly realistic forged ID
The Completely Automated Public Turing test to Tell Computers and Humans Apart (CAPTCHA) is widely deployed on the web as a security mechanism to distinguish humans from automated bots. However, their robustness is being challenged by the rapid advancements in AI, with models capable of near-human l
Automated mosquito species identification is critical for combating vector-borne diseases. We introduce Q-MoFusion, a novel hybrid quantum-classical framework that fuses deep features from pre-trained Audio Spectrogram Transformer (AST) and Whisper models using a Variational Quantum Circuit (VQC). O
Although deep networks excel on RGB images, their performance degrades sharply under severe domain shifts—such as sketch recognition, where color and texture cues are missing. In this work, we propose a novel pipeline that leverages semantic cues extracted from sketches to guide the synthesis of pho
Accurate identification of mosquito species is crucial for controlling vector-borne diseases, yet visual or acoustic methods alone are often insufficient. We propose a multimodal deep-learning framework that combines high-resolution images with wingbeat audio using a SwinV2 vision transformer and an
Deep Neural Networks (DNNs), backbone architecture in `almost' every computer vision task, are vulnerable to adversarial attacks, particularly physical out-of-distribution (OOD) adversarial patches. Existing defense models often struggle with interpreting these attacks in ways that align with human…
Readout multiplexing is a promising solution to overcome hardware limitations and data bottlenecks in imaging with single-photon detectors. Conventional multiplexed readout processing creates an upper bound on photon counts at a very fine time scale, where frames with multiple detected photons must…