5 citations · 11 across the 8 of their papers we have counts for
9 papers
PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores
Lucas Goncalves, Prashant Mathur, Chandrashekhar Lavania +3
Recent advancements in audio-visual generative modeling have been propelled by progress in deep learning and the availability of data-rich benchmarks. However, the growth is not at…
Self-supervised Speaker Recognition Training Using Human-Machine Dialogues
Metehan Cekic, Ruirui Li, Zeya Chen +3
Speaker recognition, recognizing speaker identities based on voice alone, enables important downstream applications, such as personalization and authentication. Learning speaker re…
Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations
Metehan Cekic, Can Bakiskan, Upamanyu Madhow
While end-to-end training of Deep Neural Networks (DNNs) yields state of the art performance in an increasing array of applications, it does not provide insight into, or control ov…
Sparse Coding Frontend for Robust Neural Networks
Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer +1
Deep Neural Networks are known to be vulnerable to small, adversarially crafted, perturbations. The current most effective defense methods against these adversarial attacks are var…
A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations
Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer +1
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks: carefully constructed perturbations to an image can seriously impair classification accuracy, while being imperce…
Polarizing Front Ends for Robust CNNs
Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic +2
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up str…
Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors
Metehan Cekic, Soorya Gopalakrishnan, Upamanyu Madhow
Can we distinguish between two wireless transmitters sending exactly the same message, using the same protocol? The opportunity for doing so arises due to subtle nonlinear variatio…
Robust Wireless Fingerprinting via Complex-Valued Neural Networks
Soorya Gopalakrishnan, Metehan Cekic, Upamanyu Madhow
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to dist…
Robust Adversarial Learning via Sparsifying Front Ends
Soorya Gopalakrishnan, Zhinus Marzi, Metehan Cekic +2
It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks. In this paper, we take a bottom-up signal processing perspect…