Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Mansourifar, Hadi"'
Autor:
Mansourifar, Hadi, Simske, Steven J.
Conditional Generative Adversarial Nets (CGANs) need a significantly huge dataset with a detailed pixel-wise annotation to generate high-quality images. Unfortunately, any amount of missing pixel annotations may significantly impact the result not on
Externí odkaz:
http://arxiv.org/abs/2303.11175
Autor:
Mansourifar, Hadi, Simske, Steven J.
Satellite images often contain a significant level of sensitive data compared to ground-view images. That is why satellite images are more likely to be intentionally manipulated to hide specific objects and structures. GAN-based approaches have been
Externí odkaz:
http://arxiv.org/abs/2301.11726
Autor:
Amin, Md Hasibul, Madanu, Harika, Lavu, Sahithi, Mansourifar, Hadi, Alsagheer, Dana, Shi, Weidong
Since the beginning of the vaccination trial, social media has been flooded with anti-vaccination comments and conspiracy beliefs. As the day passes, the number of COVID- 19 cases increases, and online platforms and a few news portals entertain shari
Externí odkaz:
http://arxiv.org/abs/2211.13003
Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition. It reveals the vulnerability of deep con
Externí odkaz:
http://arxiv.org/abs/2207.01149
With the high prevalence of offensive language against minorities in social media, counter-hate speeches (CHS) generation is considered an automatic way of tackling this challenge. The CHS is supposed to appear as a third voice to educate people and
Externí odkaz:
http://arxiv.org/abs/2203.03584
With the rise of voice chat rooms, a gigantic resource of data can be exposed to the research community for natural language processing tasks. Moderators in voice chat rooms actively monitor the discussions and remove the participants with offensive
Externí odkaz:
http://arxiv.org/abs/2106.13238
Hate speech detection has become a hot topic in recent years due to the exponential growth of offensive language in social media. It has proven that, state-of-the-art hate speech classifiers are efficient only when tested on the data with the same fe
Externí odkaz:
http://arxiv.org/abs/2107.02024
Autor:
Mansourifar, Hadi, Shi, Weidong
Virtual Big Data (VBD) proved to be effective to alleviate mode collapse and vanishing generator gradient as two major problems of Generative Adversarial Neural Networks (GANs) very recently. In this paper, we investigate the capability of VBD to add
Externí odkaz:
http://arxiv.org/abs/2009.08387
Autor:
Mansourifar, Hadi, Shi, Weidong
Rounding confidence score is considered trivial but a simple and effective countermeasure to stop gradient descent based image reconstruction attacks. However, its capability in the face of more sophisticated reconstruction attacks is an uninvestigat
Externí odkaz:
http://arxiv.org/abs/2009.02286
Autor:
Mansourifar, Hadi, Shi, Weidong
Fake face detection is a significant challenge for intelligent systems as generative models become more powerful every single day. As the quality of fake faces increases, the trained models become more and more inefficient to detect the novel fake fa
Externí odkaz:
http://arxiv.org/abs/2003.12244