Naman Khandelwal

Sunil Kothari, Sumukha Sharma Thoppanahalli Chandramouli, Naman Khandelwal et al.

This position paper argues that the machine learning community should prioritize early-stage quality assurance in annotation pipelines over the prevailing practice of late-stage validation. Data quality bottlenecks increasingly limit foundation model improvement, yet quality assurance research focuses almost exclusively on validation methods rather than validation timing. When validation occurs, not merely what methods are employed, fundamentally determines both error rates and annotation costs. This temporal neglect is puzzling given the well-established "shift-left" principle from software engineering, where empirical studies demonstrate 4--100x cost multipliers for defects detected in later stages (Boehm, 1981; Shull et al., 2002). Annotation pipelines exhibit analogous dynamics: errors caught before annotation begins cost a fraction of those discovered after review cycles complete. We propose a taxonomy of three QA trigger points, namely pre-annotation (T0), post-annotation (T1), and post-review (T2), that decompose annotation workflows into discrete validation opportunities. A parametric error-propagation model formalizes when timing affects final error rates versus only economics, making timing a measurable design variable rather than a configuration afterthought. A survey of 47 recent papers reveals that only 4% report when validation occurs, a striking gap given timing's demonstrated impact in adjacent fields. Without explicit attention to QA timing, the community risks optimizing validation methods while ignoring the structural variable that may matter most. Acting on this position requires three steps: researchers should report QA timing configurations alongside validation methods; annotation platforms should expose timing as a first-class parameter; and the community should run controlled experiments that measure stage-specific detection rates directly.

Sukhandeep Kaur, Mubashir Buhari, Naman Khandelwal et al.

Deepfakes offer great potential for innovation and creativity, but they also pose significant risks to privacy, trust, and security. With a vast Hindi-speaking population, India is particularly vulnerable to deepfake-driven misinformation campaigns. Fake videos or speeches in Hindi can have an enormous impact on rural and semi-urban communities, where digital literacy tends to be lower and people are more inclined to trust video content. The development of effective frameworks and detection tools to combat deepfake misuse requires high-quality, diverse, and extensive datasets. The existing popular datasets like FF-DF (FaceForensics++), and DFDC (DeepFake Detection Challenge) are based on English language.. Hence, this paper aims to create a first novel Hindi deep fake dataset, named ``Hindi audio-video-Deepfake'' (HAV-DF). The dataset has been generated using the faceswap, lipsyn and voice cloning methods. This multi-step process allows us to create a rich, varied dataset that captures the nuances of Hindi speech and facial expressions, providing a robust foundation for training and evaluating deepfake detection models in a Hindi language context. It is unique of its kind as all of the previous datasets contain either deepfake videos or synthesized audio. This type of deepfake dataset can be used for training a detector for both deepfake video and audio datasets. Notably, the newly introduced HAV-DF dataset demonstrates lower detection accuracy's across existing detection methods like Headpose, Xception-c40, etc. Compared to other well-known datasets FF-DF, and DFDC. This trend suggests that the HAV-DF dataset presents deeper challenges to detect, possibly due to its focus on Hindi language content and diverse manipulation techniques. The HAV-DF dataset fills the gap in Hindi-specific deepfake datasets, aiding multilingual deepfake detection development.