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<h1>Beyond Spatial Frequency: Pixel‑wise Temporal Frequency‑based Deepfake Video Detection</h1>
<div class="sub">Taehoon Kim, Jongwook Choi, Yonghyun Jeong, Haeun Noh, Jaejun Yoo, Seungryul Baek, Jongwon Choi*</div>
<div class="sub">Chung‑Ang Univ · NAVER Cloud · UNIST</div>
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<span class="badge highlight" title="ICCV 2025 Highlight" style="background:none;">⭐ We got <b>Highlight</b>!</span>
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<h2>Interactive Demos</h2>
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<!-- <span>shining.avi (AVI)</span> -->
<span class="source">Source: <a href="https://github.com/ZhendongWang6/AltFreezing" target="_blank" rel="noopener">AltFreezing GitHub</a></span>
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<figcaption>
<!-- <span>baby.mp4 (MP4)</span> -->
<span class="source">Source: <a href="https://www.reddit.com/r/GenAI4all/comments/1mrvx89/ai_baby_videos_are_going_viral_cute_innovation_or/" target="_blank" rel="noopener">Reddit — GenAI4all</a></span>
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<source src="figures/tiktok.mp4" type="video/mp4" />
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<figcaption>
<span class="source">Source: <a href="https://www.tiktok.com/@wildromance10/video/7266665964429708545?is_from_webapp=1&sender_device=pc&web_id=7536255224983193089" target="_blank" rel="noopener">TikTok</a></span>
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</section>
<!-- Abstract -->
<section id="abstract" class="section">
<h2>Abstract</h2>
<p>
We introduce a novel method for deepfake video detection that utilizes <b>pixel‑wise temporal frequency spectra</b>. Unlike previous approaches that stack 2D frame‑wise spatial frequency spectra, we extract pixel‑wise temporal frequency by performing a 1D Fourier transform on the time axis per pixel, effectively identifying temporal artifacts. We also propose an <b>Attention Proposal Module (APM)</b> to extract regions of interest for detecting these artifacts. Our method demonstrates outstanding generalizability and robustness in various challenging deepfake video detection scenarios.
</p>
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<!-- Method & Architecture -->
<section id="method" class="section">
<h2>Method & Architecture</h2>
<div class="grid">
<div class="card span-4">
<figure>
<img src="figures/figure2.png" alt="Temporal artifacts and pixel‑wise frequency extraction" />
<figcaption>
<span><strong>Frequency Extraction</strong>: 1D FFT per‑pixel along time captures subtle temporal artifacts.</span>
</figcaption>
</figure>
</div>
<div class="card span-8">
<figure>
<img src="figures/figure3.png" alt="Frequency Feature Extractor + Joint Transformer Module" />
<figcaption>
<span><strong>Architecture</strong>: Frequency Feature Extractor + APM → Joint Transformer for robust detection.</span>
</figcaption>
</figure>
</div>
</div>
<ul>
<li><b>Pixel‑wise Temporal Frequency</b>: 1D FFT on the time axis per pixel to capture subtle artifacts.</li>
<li><b>Attention Proposal Module (APM)</b>: Weakly‑supervised focus on temporal‑artifact regions.</li>
<li><b>Joint Transformer</b>: Fuses global/part‑based frequency signals and spatio‑temporal context.</li>
</ul>
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<h2>Experiments & Results</h2>
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<figure>
<img src="figures/figure5.png" alt="APM visualization focusing on eyes/mouth regions" />
<figcaption>
<span>APM highlights regions (eyes, mouth) where temporal incoherence is likely.</span>
</figcaption>
</figure>
</div>
<div class="card span-6">
<figure>
<img src="figures/figure1.png" alt="Video‑level AUC comparison across datasets" />
<figcaption>
<span>Consistent SOTA video‑level AUC across datasets demonstrates strong generalization.</span>
</figcaption>
</figure>
</div>
</div>
<ul>
<li>SOTA performance on FF++, CDF, DFDC, KoDF, etc.</li>
<li>Strong cross‑deepfakes and cross‑synthesis generalization.</li>
<li>APM effectively identifies temporally inconsistent regions.</li>
</ul>
</section>
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<section id="limitations" class="section">
<h2>Limitations & Future Work</h2>
<div class="grid">
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<div class="pill" style="margin-bottom:10px;">Known Limitation</div>
<ul>
<li><b>Heavy compression</b> (H.264/JPEG/WebP) merges neighboring pixels, weakening pixel‑level motion → temporal‑frequency shift.</li>
<li>Low‑frequency components align with raw signals, but <b>high‑frequency</b> components diverge after compression.</li>
</ul>
<div class="pill" style="margin:14px 0 8px;">Future Work</div>
<p>We will investigate <b>temporal‑frequency regularization</b> to mitigate compression‑induced degradation.</p>
</div>
<div class="card span-4">
<figure>
<img src="figures/figureE.png" alt="Average pixel‑wise temporal frequency under compression" />
<figcaption>
<span>Average temporal‑frequency under H.264/JPEG/WebP compression.</span>
</figcaption>
</figure>
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</section>
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<h2>📚 Citation</h2>
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<pre>@InProceedings{Kim_2025_ICCV,
author = {Kim, Taehoon and Choi, Jongwook and Jeong, Yonghyun and Noh, Haeun and Yoo, Jaejun and Baek, Seungryul and Choi, Jongwon},
title = {Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11198-11207}
}</pre>
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<p>This work was partly supported by the IITP grant funded by the Korea government (MSIT): No. RS-2025-02263841: Development of a Real-time Multimodal Framework for Comprehensive Deepfake Detection Incorporating Common Sense Error AnalysisRS-2021-II211341: Artificial Intelligence Graduate School Program (Chung-Ang University) No. RS-2020-II201336: AIGS program (UNIST) </p>
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