現今網路發達，資訊傳播快速，使得在社群媒體上分享知識技能、日常生活不再是件難事。在各式各樣的分享媒介中，影片成為許多人的選擇，用以與他人分享各式訊息，然而享受著此等便利時，創作者亦可能必須要面對資訊安全的問題。例如影片可能被非法下載、改編、分享，又如知名人物的肖像遭有心人士製成色情影片，造成他人名譽、隱私、心靈上備受侵害，或又如詐欺、假消息等問題亦層出不窮。因此，本論文除蒐集社群媒體的影片相關資訊外，也將站在保護隱私的角度，對於影片人臉的部分進行去識別化，讓上傳影片者，可以在隱私權等權利免於遭受侵害的情況下，放心地製作及分享自己的影片。

為達到上述目的，本論文利用爬蟲的方式抓取 Facebook 以及 TikTok 的公開影片資訊，並於爬取前，對影片網址做前處理，以提升爬取的效能，且能夠 100% 避免重複爬取相同網址。在蒐集資料的同時，本論文亦會針對儲存到影片資料庫的影片進行影像處理，利用人臉提取、校正，以取得影像中的人臉特徵資料（如：臉部座標、性別、年齡等），並儲存於人臉資料庫中。欲去識別化的影片則會透過物件偵測與人臉提取的方式獲取人臉特徵資料，然後與人臉資料庫既有的資料進行比對，篩選出最為合適的來源人臉，再利用特徵融合的方式處理欲進行去識別化的影片。經實驗後，本系統產生的結果不僅不會被認定是相同的人，與未先進行影片比對即直接進行去識別化的影片相比，也能有效降低被認為是造假的機率。

The widespread availability of the internet has made it simple for people to share their knowledge, skills, and daily experiences on social media platforms via videos. However, with this convenience comes the risk of information security breaches, such as the illegal downloading, modification, and sharing of videos, as well as the use of celebrities' images in pornographic videos, which can harm their reputation, privacy, and psychological well-being. Furthermore, scams and fake news are abundant on social media. To address these concerns, our paper aims to collect video-related information on social media and de-identify faces in videos to safeguard privacy and other rights, while still allowing creators to share their videos.

We use web crawlers to collect public video data from Facebook and TikTok. We pre-process URLs to avoid repetitive crawling and increase efficiency. During video collection, we extract and align faces to generate facial data such as facial coordinates, gender, and age, which are stored in a Face Database. To de-identify the video, we perform object detection and face extraction to collect facial data. We then compare this data with existing facial database data to select the most suitable source face for the feature fusion-based de-identification process. According to this study, our system has been found to effectively produce non-identifiable results in experiments and significantly reduces the chances of being identified as fake when compared to de-identified videos without prior video matching.

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