尺度不變特徵與半色調視訊之安全議題

Abstract

隨著行動裝置與網路技術的進步，雲端服務提供者開始為各種行動裝置的使 用者提供服務。其中以多媒體的各種應用最為廣泛。這種新型態的多媒體應用逐 漸改變人類的生活;此外，也帶來許多多媒體應用上的安全性議題。本論文將逐一 討論這些由雲端與行動環境中的多媒體應用所衍生出的安全性議題。 對於一個行動裝置而言，視訊半色調技術掌握電子紙顯示器能撥放視訊的關 鍵，且在近年受到相當程度的重視。在本論文中，我們提出一種新的半色調視訊 編碼技術，應用在新型行動顯示裝置的電子紙中，為了避免可能發生在半色調視 訊中的閃爍及其他視覺現象，我們提出平均閃爍估算將視訊品質與平均閃爍樂最 佳化。此外，提出的最佳化技術也可以應用在各種不同的半色調視訊產生技術 中。當電子紙與雲端環境的多媒體應用結合後，所遭遇到的第一個安全性問題便 是在電子紙中撥放的視訊是否通過認證。由於視訊經過雲端傳播，視訊可能經過 竄改而惡意傳遞錯誤訊息，因此在視訊或影像撥放之前，必須先經由視訊認證技 術確認接收到的視訊是正確無誤的;為此，我們在論文中提出基於壓縮感測的半色 調視訊認證技術。 在各種多媒體雲端服務中，影像比對的應用最為廣泛，如人臉辨識，臉孔 偵測，圖形識別等，而這些技術多數是基於影像特徵抽取技術;對此，我們進一 步指出目前在多媒體領域中常使用的特徵抽取技術的缺點，由於目前常用的多 媒體安全技術如浮水印技術，複製偵測技術或影像認證技術皆利用尺度不變特 徵(SIFT)等特徵抽取技術來實現，而其本身的安全性缺點將使基於該技術發展出 的各種應用有安全性的疑慮，在本論文中，我們首先證明了此類特徵抽取技術特 徵點可以被輕易地移除，導致該技術在各種安全性應用中失效。此外，我們更利 用非線性規畫方式將移除特徵點所產生的失真降到最低，以此計算特徵點的移除所需付出的失真;接著我們會提出如何改善此一缺點，發展出具有安全性的特徵抽 取技術。 最後，當雲端技術開始普及，使用者將大量的多媒體資料儲存於伺服器中， 在這樣的環境下使用者的隱私很有可能遭到洩漏，因此，我們提出一種在能應 用在雲端環境中，基於加密域的特徵抽取技術;藉由此技術，我們可以在已加密 的影像中，不需將影像解密即可直接抽取出加密的特徵做比對，過程中不會洩 漏影像的資訊，如此，伺服器在不知道影像內容的情況下便可達到影像比對的 目的，使用者的隱私權便可以得到保障。為達到此目的，我們首先提出安全比 對，所提出的安全比對提供兩個加密值在不需解密的情況下得知兩個相對應解 密值的大小，以此安全比對技術為基礎，再配合加密方法中原有的加法同態特 性(Homomorphism)，許多既有的演算法便可在加密域中被實現。

With the advances in mobile device and cloud computing technologies, cloud computing provider focuses on providing various multimedia applications as services to mobile device users. The new type of multimedia application is changing people’s life. On the other hand, it also lead to several multimedia security issues. In this dissertation, the multimedia security issues will be described. For a mobile device, video halftoning is a key technology for use in electronic paper (e-paper) or smart paper, which is an emerging display device that has received considerable attention recently. In this dissertation, a temporal frequency of flickering-distortion optimized video halftoning method is proposed. We first uncover three visual defects that conventional neighboring frame referencing-based video halftoning methods, due to their sequential changes of reference frames, will encounter. To deal with the problem, we then propose a reference frame update per GOP-based error diffusion video halftoning method based on a flickering sensitivity-based human visual model. To efficiently compromise between average temporal frequency of flickering (ATFoF) and visual quality, temporal frequency of flickering-distortion (TFoFD) is presented as a metric for video halftoning performance evaluation. Based on the proposed probability model of video halftoning, the TFoFD curve can be accurately estimated to optimize the tradeoff between quality and ATFoF before the video is halftoned. Our temporal frequency of flickering-distortion optimization strategy can also be applied to other video halftoning schemes for performance improvement. With the advances in mobile device and cloud computing technologies, people are getting used to accessing and querying multimedia data in the cloud environment. Scale space image feature extraction (SSIFE) has been widely adopted in multimedia security and other applications for cloud service. However, the security threat to SSIFE-based media security applications, which will be addressed in this thesis, is relatively unexplored. The security threat, composed of a constrained-optimization keypoint inhibition attack (KIHA) and a keypoint insertion attack (KISA), is specifically designed in the proposed method for scale-space feature extraction methods such as SIFT and SURF. The principle of KIHA is to make a fool of feature extraction protocols in that the detection rules are purposely violated so that no local maximum can be found around in a local region. On the other hand, KISA is designed to create the false positive problem. Our method is evaluated and compared with Do et al.’s method (ACM MM’10), which also figures out the weakness of our previous work (ACM MM’09). In addition, our proposed security threat is applied to an image copy detection method operated on a web-scale image database for performance evaluation. In addition, privacy has received considerable attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario where the server is resource-abundant and is capable of finishing the designated tasks. It is envisioned that secure media applications with privacy preservation will be seriously treated. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this dissertation is the first to target the importance of privacy-preserving SIFT (PPSIFT) and to address the problem of secure SIFT feature extraction and representation in the encrypted domain. As all of the operations in SIFT must be moved to the encrypted domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic encryption. In our method, homomorphic comparison is a key component for PPSIFT feature detection, but it is still a challenging issue for homomorphic encryption methods, like the Paillier cryptosystem. To solve this problem, the idea here is to investigate a homomorphic comparison strategy via quantization. We also analyze the error probability of feature extraction due to a scaling factor being introduced to realize an integer DoG transform in the Paillier cryptosystem. Moreover, we show through the security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure against ciphertext only attack and known plaintext attack. Experimental results obtained from different case studies demonstrate that the proposed homomorphic encryption-based privacy-preserving SIFT performs comparably to original SIFT and that our method is useful in SIFT-based privacy-preserving applications.