153 research outputs found
Data Fine-tuning
In real-world applications, commercial off-the-shelf systems are utilized for
performing automated facial analysis including face recognition, emotion
recognition, and attribute prediction. However, a majority of these commercial
systems act as black boxes due to the inaccessibility of the model parameters
which makes it challenging to fine-tune the models for specific applications.
Stimulated by the advances in adversarial perturbations, this research proposes
the concept of Data Fine-tuning to improve the classification accuracy of a
given model without changing the parameters of the model. This is accomplished
by modeling it as data (image) perturbation problem. A small amount of "noise"
is added to the input with the objective of minimizing the classification loss
without affecting the (visual) appearance. Experiments performed on three
publicly available datasets LFW, CelebA, and MUCT, demonstrate the
effectiveness of the proposed concept.Comment: Accepted in AAAI 201
Uncovering the Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects
Audio has become an increasingly crucial biometric modality due to its
ability to provide an intuitive way for humans to interact with machines. It is
currently being used for a range of applications, including person
authentication to banking to virtual assistants. Research has shown that these
systems are also susceptible to spoofing and attacks. Therefore, protecting
audio processing systems against fraudulent activities, such as identity theft,
financial fraud, and spreading misinformation, is of paramount importance. This
paper reviews the current state-of-the-art techniques for detecting audio
spoofing and discusses the current challenges along with open research
problems. The paper further highlights the importance of considering the
ethical and privacy implications of audio spoofing detection systems. Lastly,
the work aims to accentuate the need for building more robust and generalizable
methods, the integration of automatic speaker verification and countermeasure
systems, and better evaluation protocols.Comment: Accepted in IJCAI 202
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