Attacking the OutGuess

Jessica Fridrich, Miroslav Goljan & Dorin Hogea
"Attacking the OutGuess,"
ACM Workshop on Multimedia and Security 2002,
Juan-les-Pins, France, December 6, 2002





Abstract
In this paper, we describe new methodology for developing steganalytic methods for JPEG images. The proposed framework can be applied to virtually all current methods for JPEGs including OutGuess, F5, and J-Steg. It also enables accurate estimation of the length of the embedded secret message. The methodology is demonstrated on OutGuess 0.2.

這篇論文針對 OutGuess 0.2 隱藏工具提出一套攻擊方法。所提的分析架構, 不但可以運用到不同的JPEG影像隱藏工具, 亦可以正確地估算嵌入訊息的長度。

在這篇論文的第三節中提到 OutGuess 的特點, 與解釋為什麼 Chi-square attack 無法破解 OutGuess。

p2. left column
The OutGuess steganographic algorithm was proposed by Neils Provos to counter the statistical chi-square attack. In the first pass, similar to J-Steg, OutGuess embeds message bits along a random walk into the LSBs of coefficients while skipping 0’s and 1’s. After embedding, the image is processed again using a second pass. This time, corrections are made to the coefficients to make the stego image histogram match the cover image histogram. Because the chi-square attack is based on analyzing first-order statistics of the stego image, it cannot detect messages embedded using OutGuess. Provos also reports that the corrections are made in such a manner to avoid detection using his generalized chi-square attack.

本篇論文的核心觀念描述在底下這段文字:

P.2 right column
Because OutGuess introduces random changes into the quantized coefficients, the spatial discontinuities at the boundaries of all 8×8 blocks will increase. We will measure the discontinuity using the blockiness measure. For detection, we will inspect the increase of this blockiness measure after embedding a 100% message again using OutGuess. This increase will be smaller for the stego image than for the cover image because of the partial cancellation of changes. This difference will form the basis of our message length estimation.

由於 OutGuess 隨機改變量化後 DCT 係數的 LSBs, 增加了還原後 8*8 區塊的不連續性。J. Fridrich用一個 blockiness formula 來估算區塊不連續性。使用 OutGuess 藏入100% 的資料量後, 檢驗區塊不連續性的增加量。如果這張影像是偽裝影像(stego image), 區塊不連續性增加量是比原始掩護影像 (cover image) 來得小。這個差距就是用來估算嵌入資料量的基礎。

而論文中的 blockiness formula, 其實就是不同區塊間的相鄰像素間的色彩差距絕對值的總和。再求取嵌入資料量大小的過程中, 必須知道原始掩護影像的區塊不連續性, S(0)。J. Fridrich 經由實驗證明, 將偽裝影像裁去四列像素後所得的區塊不連續性和原始掩護影像的區塊不連續性值是相近的, 因此可以用來代替所需的 S(0)。