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<body>
  <!-- Topbar -->
  <div class="topbar" role="navigation" aria-label="Primary">
    <div class="brand">
      <img src="figures/ICCV_2025.png" alt="ICCV 2025" style="height:60px;width:auto;border-radius:6px;border:1px solid var(--border);background:#fff;" />
    </div>
    <nav class="nav">
      <a href="#demos">Demos</a>
      <a href="#abstract">Abstract</a>
      <a href="#method">Method</a>
      <a href="#experiments">Experiments</a>
      <a href="#limitations">Limitations</a>
      <a href="#citation">Citation</a>
      <a href="#ack">Acknowledgement</a>
    </nav>
    <div class="actions">
      <label class="toggle" title="Toggle theme">
        <input type="checkbox" id="modeToggle" />
        <span>🌗 Theme</span>
      </label>
    </div>
  </div>

  <!-- Hero -->
  <header class="hero">
    <div style="text-align:center;">
      <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>
    </div>
    <div style="display: flex; align-items: center; gap: 18px; margin-bottom: 18px; justify-content: center;">
      <img src="figures/CAU.png" alt="Chung-Ang University" style="height:40px;width:80px;object-fit:contain;border-radius:6px;background:#fff;border:none;" />
      <img src="figures/NAVERCLOUD.png" alt="NAVER Cloud" style="height:70px;width:140px;object-fit:contain;border-radius:6px;background:#fff;border:none;" />
      <img src="figures/UNIST.png" alt="UNIST" style="height:50px;width:100px;object-fit:contain;border-radius:6px;background:#fff;border:none;" />
    </div>
    <div class="cta" style="display: flex; justify-content: center; gap: 12px;">
      <a class="btn" href="https://github.com/rama0126/PwTF-DVD" target="_blank" rel="noopener" aria-label="GitHub Repository" style="display: flex; align-items: center;">
        <svg width="20" height="20" viewBox="0 0 16 16" fill="currentColor" style="vertical-align:middle;margin-right:6px;" aria-hidden="true">
          <path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38
            0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52
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        </svg>
        GitHub
      </a>
      <a class="btn" href="https://arxiv.org/abs/2507.02398" target="_blank" rel="noopener" aria-label="arXiv" style="display:flex;align-items:center;gap:6px;">
        <svg width="20" height="20" viewBox="0 0 32 32" fill="none" style="vertical-align:middle;" aria-hidden="true">
          <rect width="32" height="32" rx="6" fill="#B31B1B"/>
          <text x="16" y="22" text-anchor="middle" font-size="15" font-family="Arial, Helvetica, sans-serif" fill="#fff" font-weight="bold">arXiv</text>
        </svg>
        arXiv
      </a>
    <a class="btn" href="#" style="display:flex;align-items:center;gap:6px;pointer-events:none;opacity:0.6;background:#eee;color:#444;">
      <svg width="20" height="20" viewBox="0 0 32 32" fill="none" style="vertical-align:middle;" aria-hidden="true">
        <rect width="32" height="32" rx="6" fill="#1B6CB3"/>
        <text x="16" y="22" text-anchor="middle" font-size="15" font-family="Arial, Helvetica, sans-serif" fill="#fff" font-weight="bold">ICCV</text>
      </svg>
      ICCV paper
    </a>
    </div>

    <div style="display: flex; justify-content: flex-end;">
      <span class="badge highlight" title="ICCV 2025 Highlight" style="background:none;">⭐ We got <b>Highlight</b>!</span>
    </div>

  </header>

  <!-- DEMOS -->
  <section id="demos" class="section">
    <h2>Interactive Demos</h2>

    <div class="media-slider" aria-label="Deepfake demo carousel">
      <button class="arrow prev" aria-label="Previous">◀</button>
      <div class="slides" id="slides" tabindex="0">
        <!-- Slide 1: Shining (AVI) -->
        <div class="slide" data-index="0">
          <figure>
            <div class="player">
              <div style="position:relative;">
                <video class="demo-video" controls playsinline webkit-playsinline x5-playsinline preload="metadata" oncontextmenu="return false;" controlsList="nodownload">
                  <source src="figures/shining.mp4" type="video/mp4" />
                </video>
              </div>
            </div>
            <figcaption>
              <!-- <span>shining.avi (AVI)</span> -->
              <span class="source">Source: <a href="https://github.com/ZhendongWang6/AltFreezing" target="_blank" rel="noopener">AltFreezing GitHub</a></span>
            </figcaption>
          </figure>
        </div>

        <!-- Slide 2: Baby (MP4) -->
        <div class="slide" data-index="1">
          <figure>
            <div class="player">
              <div style="position:relative;">
                <video class="demo-video" controls playsinline webkit-playsinline x5-playsinline preload="metadata" oncontextmenu="return false;" controlsList="nodownload">
                  <source src="figures/baby.mp4" type="video/mp4" />
                </video>

              </div>
            </div>
            <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>
            </figcaption>
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              <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.
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  </section>

  <!-- 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>
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      <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>
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    </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>
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    <h2>Experiments & Results</h2>
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          <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>
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        <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>
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    <h2>Limitations & Future Work</h2>
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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>
      </div>
    </div>
  </section>

  <!-- Citation -->
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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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    <h2>🙏 Acknowledgement</h2>
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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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