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<!DOCTYPE html>
<html lang="en">
<head>
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    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>DASH LAB - Datasets</title>
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<body class="bg-gray-50 flex flex-col min-h-screen">
    <div class="container mx-auto px-4 pt-8 max-w-6xl flex-grow">

        <!-- Language Link -->
        <p class="text-right mb-5">
            <a href="Datasets_kor" class="text-blue-700 font-bold hover:underline">한국어 버전 →</a>
        </p>

        <!-- COCO Spliced Datasets -->
        <div class="section">
            <div class="dash-card-dataset">
                <h3 class="dash-header-dataset">📦 COCO Spliced Datasets</h3>
                <p class="leading-relaxed text-gray-700 mb-4">We utilized the <a href="https://cocodataset.org/#home" target="_blank" class="text-blue-600 hover:text-blue-800">COCO dataset</a> to generate a manipulated dataset. Given that the dataset comes with provided labels (masks), we initially identified the desired portions in the original images by applying the mask to them. Subsequently, we used these specific regions to manipulate other images. Each image was altered with approximately 8 to 10 objects, resulting in a total of around 900k manipulated images.</p>

                <p class="text-center mt-5">
                    <img loading="lazy" src="" onerror="this.src=getImg('/img/Publications/Screen Shot 2023-12-11 at 2.15.08 PM.png')" alt="COCO Spliced Example" class="max-w-full h-auto rounded-lg shadow-lg mx-auto" />
                </p>
            </div>
        </div>

        <!-- Satellite Forgery Image Dataset -->
        <div class="section">
            <div class="dash-card-dataset">
                <h3 class="dash-header-dataset">🛰️ Satellite Forgery Image Dataset</h3>
                <p class="leading-relaxed text-gray-700 mb-4">We used <a href="http://deepglobe.org/" target="_blank" class="text-blue-600 hover:text-blue-800">DeepGlop dataset</a> to create Satellite Forgery images by following the method proposed in <a href="https://openaccess.thecvf.com/content_CVPRW_2020/papers/w39/Horvath_Manipulation_Detection_in_Satellite_Images_Using_Deep_Belief_Networks_CVPRW_2020_paper.pdf" target="_blank" class="text-blue-600 hover:text-blue-800">Deep Belief networks</a>. A total of 293 orthorectified images with an image resolution of $1000 \times 1000$ pixels were collected. We use 100 of the 293 orthorectified images to create manipulated images. 19 different objects are spliced into the 100 images generating a total of 500 manipulated images with their corresponding manipulation ground truth masks. The 19 objects include rockets, planes, and drone images. The figure shown below illustrates some examples from the manipulated dataset.</p>

                <p class="text-center mt-5">
                    <img loading="lazy" src="" onerror="this.src=getImg('/img/Publications/satellite_forgery.png')" alt="Satellite Forgery Example" class="max-w-full h-auto rounded-lg shadow-lg mx-auto" />
                </p>
            </div>
        </div>

        <!-- RWDF-23 Dataset -->
        <div class="section">
            <div class="dash-card-dataset">
                <h3 class="dash-header-dataset">🎬 RWDF-23 Dataset</h3>
                <p class="leading-relaxed text-gray-700 mb-4">The RWDF-23 is collected from the wild, consisting of 2,000 deepfake videos collected from 4 platforms targeting 4 different languages span created from 21 countries: Reddit, YouTube, TikTok, and Bilibili. By expanding the dataset's scope beyond the previous research, we capture a broader range of real-world deepfake content, reflecting the ever-evolving landscape of online platforms.</p>

                <div class="mt-5 p-4 bg-blue-100 rounded-lg border-l-4 border-blue-600 text-center">
                    <strong class="text-gray-800">📝 To obtain the dataset, please fill out the form <a href="https://docs.google.com/forms/d/e/1FAIpQLScsxskSEI0LkmUdI7ClAqs-xslyviDNoKHhiZC3FsBqFG4NJA/viewform" target="_blank" class="text-blue-700 hover:text-blue-900 underline">HERE</a></strong>
                </div>

                <p class="text-center mt-5">
                    <img loading="lazy" src="" onerror="this.src=getImg('/img/Publications/rwdf23_cikm23.png')" alt="RWDF-23 Example" class="max-w-full h-auto rounded-lg shadow-lg mx-auto" />
                </p>
            </div>
        </div>

        <!-- FakeAVCeleb Dataset -->
        <div class="section">
            <div class="dash-card-dataset">
                <h3 class="dash-header-dataset">🎭 FakeAVCeleb Dataset</h3>
                <p class="leading-relaxed text-gray-700 mb-4">In FakeAVCeleb, we propose a novel Audio-Video Deepfake dataset that contains synthesized lip-synced fake audios. To generate a more realistic dataset, we selected real YouTube videos of celebrities having four racial backgrounds (Caucasian, Black, East Asian, and South Asian) to counter the racial bias issue.</p>

                <p class="text-center mt-5">
                    <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/FakeAVCeleb/fakeceleb_nips2021.png')" alt="FakeAVCeleb Example" class="max-w-full h-auto rounded-lg shadow-lg mx-auto" />
                </p>
            </div>
        </div>

        <!-- VFP290K Dataset -->
        <div class="section">
            <div class="dash-card-dataset">
                <h3 class="dash-header-dataset">🚨 VFP290K Dataset</h3>
                <p class="leading-relaxed text-gray-700 mb-4">Vision-based Fallen Person (VFP290K) dataset consists of 294,714 frames of fallen persons extracted from 178 videos from 49 backgrounds, composing 131 scenes. We empirically demonstrate the effectiveness of the features through extensive experiments comparing the performance shift based on object detection models. In addition, we evaluate our VFP290K dataset with properly divided datasets by measuring the performance of fallen person detecting systems.</p>

                <div class="mt-4 p-4 bg-yellow-100 rounded-lg border-l-4 border-yellow-600">
                    <strong class="text-gray-800">🏆 We ranked first in the first round of the anomalous behavior recognition track of AI Grand Challenge 2020, South Korea, using our VFP290K dataset, which can further extend to other applications, such as intelligent CCTV or monitoring systems, as well.</strong>
                </div>

                <p class="text-center mt-5">
                    <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/VFP.JPG')" alt="VFP290K Example" class="max-w-full h-auto rounded-lg shadow-lg mx-auto" />
                </p>
            </div>
        </div>

        <!-- SKKU AGC Anomaly Detection Dataset -->
        <div class="section">
            <div class="dash-card-dataset">
                <h3 class="dash-header-dataset">📹 SKKU AGC Anomaly Detection Dataset</h3>
                <p class="leading-relaxed text-gray-700 mb-6">SKKU AGC Anomaly Detection Dataset was acquired with a stationary camera mounted at an elevation, overlooking pedestrians, both day and night from various locations. Abnormal event is when a person's head touches the ground. The data was split into detection data and classification data.</p>

                <div class="grid md:grid-cols-2 gap-8 mb-10">
                    <!-- Column 1: Detection Info -->
                    <div class="p-5 bg-blue-50/50 rounded-xl border border-blue-100">
                        <h5 class="text-blue-700 font-bold mb-3 flex items-center gap-2">
                            <i class="fas fa-search"></i> 1. Detection Data
                        </h5>
                        <p class="text-sm leading-relaxed text-gray-700">Images 1920x1080 are split into folders for day and night. Annotations are provided in XML format.</p>
                        <div class="mt-4 flex gap-4 text-xs font-bold uppercase tracking-wider text-blue-900">
                            <span class="bg-white px-2 py-1 rounded shadow-sm border border-blue-200">Day: 3000</span>
                            <span class="bg-white px-2 py-1 rounded shadow-sm border border-blue-200">Night: 2000</span>
                        </div>
                    </div>
                    <!-- Column 2: Classification Info -->
                    <div class="p-5 bg-blue-50/50 rounded-xl border border-blue-100">
                        <h5 class="text-blue-700 font-bold mb-3 flex items-center gap-2">
                            <i class="fas fa-th-large"></i> 2. Classification Data
                        </h5>
                        <p class="text-sm leading-relaxed text-gray-700">Manually cropped human subjects. Classes include normal and falldown across day/night scenarios.</p>
                        <div class="mt-4 grid grid-cols-2 gap-2 text-[10px] font-bold uppercase text-blue-900">
                            <span class="bg-white px-2 py-1 rounded shadow-sm border border-blue-200">Normal Day: 3200</span>
                            <span class="bg-white px-2 py-1 rounded shadow-sm border border-blue-200">Normal Night: 1300</span>
                            <span class="bg-white px-2 py-1 rounded shadow-sm border border-blue-200">Fall Day: 3700</span>
                            <span class="bg-white px-2 py-1 rounded shadow-sm border border-blue-200">Fall Night: 900</span>
                        </div>
                    </div>
                </div>

                <hr class="my-8 border-gray-200" />

                <!-- Detection Gallery -->
                <h5 class="text-gray-800 font-bold mb-4 flex items-center gap-2">
                    <span class="w-1.5 h-6 bg-blue-600 rounded-full"></span> Detection Examples
                </h5>
                <div class="grid grid-cols-2 md:grid-cols-3 gap-4 mb-12">
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-video bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_detection.jpg')" class="w-full h-full object-cover hover:scale-105 transition-transform duration-500" alt="AGC Detection Example 1" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-video bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_detection3.jpg')" class="w-full h-full object-cover hover:scale-105 transition-transform duration-500" alt="AGC Detection Example 2" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-video bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_detection4.jpg')" class="w-full h-full object-cover hover:scale-105 transition-transform duration-500" alt="AGC Detection Example 3" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-video bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_detection1.jpg')" class="w-full h-full object-cover hover:scale-105 transition-transform duration-500" alt="AGC Detection Example 4" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-video bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_detection2.jpg')" class="w-full h-full object-cover hover:scale-105 transition-transform duration-500" alt="AGC Detection Example 5" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-video bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_detection5.jpg')" class="w-full h-full object-cover hover:scale-105 transition-transform duration-500" alt="AGC Detection Example 6" />
                    </div>
                </div>

                <!-- Classification Gallery -->
                <h5 class="text-gray-800 font-bold mb-4 flex items-center gap-2">
                    <span class="w-1.5 h-6 bg-blue-600 rounded-full"></span> Classification Examples
                </h5>
                <div class="grid grid-cols-3 sm:grid-cols-4 md:grid-cols-6 gap-3">
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-square bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_classification.jpg')" class="w-full h-full object-cover hover:scale-110 transition-transform duration-500" alt="AGC Classification Example 1" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-square bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_classification1.jpg')" class="w-full h-full object-cover hover:scale-110 transition-transform duration-500" alt="AGC Classification Example 2" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-square bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_classification2.jpg')" class="w-full h-full object-cover hover:scale-110 transition-transform duration-500" alt="AGC Classification Example 3" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-square bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_classification3.jpg')" class="w-full h-full object-cover hover:scale-110 transition-transform duration-500" alt="AGC Classification Example 4" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-square bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_classification4.jpg')" class="w-full h-full object-cover hover:scale-110 transition-transform duration-500" alt="AGC Classification Example 5" />
                    </div>
                    <div class="overflow-hidden rounded-lg shadow-sm border border-gray-200 aspect-square bg-gray-100">
                        <img loading="lazy" src="" onerror="this.src=getImg('img/datasets/VFP290k/AGC_classification5.jpg')" class="w-full h-full object-cover hover:scale-110 transition-transform duration-500" alt="AGC Classification Example 6" />
                    </div>
                </div>
            </div>
        </div>

        <!-- Deepfake Inspector PORTAL SECTION -->
        <div class="section mt-12 mb-16">
            <div class="dash-card-dataset overflow-hidden border-2 border-blue-200">
                <div class="grid grid-cols-1 lg:grid-cols-12 gap-8 items-center">

                    <!-- Content Side -->
                    <div class="lg:col-span-7">
                        <div class="flex items-center gap-3 mb-4">
                            <span class="bg-blue-600 text-white text-[10px] font-bold px-2 py-1 rounded tracking-widest uppercase shadow-sm">Tool Beta</span>
                            <h3 class="text-2xl font-bold text-gray-800 m-0">🔍 Interactive Deepfake Inspector</h3>
                        </div>

                        <p class="text-gray-600 leading-relaxed mb-6 text-sm md:text-base">
                            Beyond providing datasets, we offer an interactive workstation for real-time analysis. Use our <strong>Histogram Analysis</strong> tool to reveal hidden manipulation artifacts and edge inconsistencies. Select the workstation on the right to begin.
                        </p>

                        <!-- Feature Grid -->
                        <div class="grid grid-cols-1 sm:grid-cols-3 gap-3">
                            <div class="bg-blue-50/50 p-3 rounded-lg flex items-center gap-3 border border-blue-100">
                                <i class="fas fa-chart-bar text-blue-600 text-sm"></i>
                                <span class="text-[10px] font-bold text-blue-900 uppercase">Histogram analysis</span>
                            </div>
                            <div class="bg-blue-50/50 p-3 rounded-lg flex items-center gap-3 border border-blue-100">
                                <i class="fas fa-search-plus text-blue-600 text-sm"></i>
                                <span class="text-[10px] font-bold text-blue-900 uppercase">Adjustable Zoom</span>
                            </div>
                            <div class="bg-blue-50/50 p-3 rounded-lg flex items-center gap-3 border border-blue-100">
                                <i class="fas fa-camera text-blue-600 text-sm"></i>
                                <span class="text-[10px] font-bold text-blue-900 uppercase">Evidence Snapshots</span>
                            </div>
                        </div>
                    </div>

                    <!-- Visual Side: Redesigned as a prominent "Action" card -->
                    <div class="lg:col-span-5 w-full">
                        <a href="Foren_ins.html" class="block group">
                            <div class="relative bg-gray-900 rounded-2xl p-8 aspect-video flex flex-col items-center justify-center border-4 border-gray-800 shadow-2xl overflow-hidden transition-all duration-300 group-hover:border-blue-600 group-hover:shadow-blue-500/30">
                                <!-- Modern Scanning Line -->
                                <div class="absolute top-0 left-0 w-full h-1 bg-blue-500/50 shadow-[0_0_15px_rgba(59,130,246,0.8)] animate-scan z-20"></div>

                                <!-- Background Mesh -->
                                <div class="absolute inset-0 opacity-10 bg-[url('https://www.transparenttextures.com/patterns/carbon-fibre.png')]"></div>

                                <!-- Central Interactive UI -->
                                <div class="relative z-10 text-center">
                                    <div class="w-24 h-24 rounded-full border-2 border-blue-500/30 flex items-center justify-center mb-4 mx-auto relative group-hover:scale-110 transition-transform duration-500">
                                        <!-- Pulsing Glow -->
                                        <div class="absolute inset-0 rounded-full bg-blue-500/20 animate-ping"></div>
                                        <i class="fas fa-microscope text-5xl text-blue-400 group-hover:text-white transition-colors"></i>
                                    </div>

                                    <div class="space-y-1">
                                        <span class="text-white text-xs font-bold uppercase tracking-[0.2em] group-hover:text-blue-400 transition-colors">Launch Station</span>
                                        <p class="text-[9px] font-mono text-blue-300/60 uppercase tracking-widest">Histogram UI · v1.0b</p>
                                    </div>
                                </div>

                                <!-- Clear Visual Hint -->
                                <div class="absolute bottom-4 left-0 w-full text-center">
                                    <span class="text-[10px] text-gray-500 uppercase font-bold tracking-tighter animate-pulse group-hover:text-blue-200">
                                        <i class="fas fa-mouse-pointer mr-1"></i> Click to Enter Analysis Mode
                                    </span>
                                </div>
                            </div>
                        </a>
                    </div>

                </div>
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        @keyframes scan {
            0% { top: 0%; opacity: 0; }
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            100% { top: 100%; opacity: 0; }
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            animation: scan 3s linear infinite;
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