6 papers
Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream
Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan Kurban
Detectors for AI-generated video are evaluated offline. A clip is decoded to pixels and scored once, increasingly by a large vision-language model. Detection, however, is deployed…
The Spectrum Is Not Enough: When Context Helps Time-Series Forecasting
Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan Kurban
A growing family of indices scores how predictable a series is from its spectrum. Practitioners increasingly read these scores as answering a different question: whether \emph{addi…
Auditing Generalization in AI-Generated Video Detection: A Six-Control Protocol and the VidAudit Toolkit
Mert Onur Cakiroglu, Zhihe Lu, Mehmet Dalkilic +1
AI-generated video detection benchmarks such as GenVidBench and AIGVDBench are the de facto leaderboards, yet most evaluation protocols leave uncontrolled confounds that can inflat…
LGQ: Learnable Geometric Quantization for Image Tokenization
Idil Bilge Altun, Mert Onur Cakiroglu, Elham Buxton +2
Recent collapse-free quantizers such as FSQ achieve stable training by replacing the learnable codebook with an engineered geometry: a fixed scalar grid whose structure is dictated…
Temporal Realism Evaluation of Generated Videos Using Compressed-Domain Motion Vectors
Mert Onur Cakiroglu, Idil Bilge Altun, Zhihe Lu +2
Temporal realism remains a central weakness of current generative video models, as most evaluation metrics prioritize spatial appearance and offer limited sensitivity to motion. We…
Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting
Mert Onur Cakiroglu, Idil Bilge Altun, Mehmet Dalkilic +2
Time series forecasting remains a challenging task for foundation models due to temporal heterogeneity, high dimensionality, and the lack of inherent symbolic structure. In this wo…