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Found 8 papers, 2 papers with code
Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
This is achieved by generating code in response to a given question using different variants.
Robustness Evaluation in Hand Pose Estimation Models using Metamorphic Testing
We found that on average more than 80\% of the hands could not be identified by BodyHands, and at least 50\% of hands could not be identified by MediaPipe hands when diagonal motion blur is introduced, while an average of more than 50\% of strongly underexposed hands could not be correctly estimated by NSRM hand.
ASDF: A Differential Testing Framework for Automatic Speech Recognition Systems
To address these limitations, our tool incorporates two novel features: (1) a text transformation module to boost the number of generated test cases and uncover more errors in ASR systems and (2) a phonetic analysis module to identify on which phonemes the ASR system tend to produce errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Detecting Depression on Twitter
Our findings open up a promising direction for detecting depression on social media with a smaller number of posts for inference, towards creating solutions for a cost-effective and timely intervention.
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors
We performed our evaluations on MesoInception-4 and TwoStreamNet models, which are the state-of-the-art deepfake detection models.
Fairness Evaluation in Deepfake Detection Models using Metamorphic Testing
Metamorphic relations are derived based on the notion that realistic perturbations of the input images, such as makeup, involving eyeliners, eyeshadows, blushes, and lipsticks (which are common cosmetic appearance) applied to male and female images, should not alter the output of the model by a huge margin.
SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Depression Detection on Twitter
Therefore, this study proposes a Stacked Embedding Recurrent Convolutional Neural Network (SERCNN) for a more optimized prediction that has a better trade-off between the number of posts and accuracy.
Robustness Evaluation of Stacked Generative Adversarial Networks using Metamorphic Testing
Synthesising photo-realistic images from natural language is one of the challenging problems in computer vision.
