Watermark based Recovery of Tampered Documents

Transcription

1 Watermark based Recovery of Tampered Documents Anamitra Makur Electrical & Electronic Engineering Nanyang Technological University, Singapore Govindarajan Sridharan Electrical & Electronic Engineering Nanyang Technological University, Singapore ABSTRACT In this work recovery of text documents is addressed. Text documents are watermarked using pixel flipping based watermarking and self-embedding for not only authentication but eventual recovery in presence of tampering. A scheme is proposed that recovers against all possible editing attacks such as substitution, insertion and deletion of characters. Algorithms for tamper detection and recovery are suggested and their functioning explained with detailed examples. Categories and Subject Descriptors K.6.5 [Security and Protection]: Authentication. I.7.m [Document and Text Processing]: Miscellaneous. General Terms Security, Algorithms. Keywords Document watermarking, authentication, recovery. 1. INTRODUCTION Documents represent a primary form of written communication and large volumes are exchanged daily. Document recipients should be able to authenticate documents as well as identify document owners. While document can be gray-scale or color images, in this work we consider documents as binary images, such as a fax. Many watermarking schemes have been developed for gray-scale and color images. However, these watermarking schemes cannot be applied to binary document images, since such schemes achieve embedding by small changes to the gray/color pixel values which is not possible for binary values, and also since perceptible image distortion of binary images (see section 2.3) is quite different from that of gray/color images. The goal of fragile watermarking in this work is to detect any tampering and to recover the original information. Documents are intercepted and tampered by non-intended users or users with malicious intent for purposes such as systematic manipulation of contents of the document for one s favor. Since the original document is seldom accessible at the decoder in applications of data authentication, the only resort to authenticate and recover the document is to extract the watermark from the non-tampered part of the document. te that the goal of watermarking is different from that of encryption, since a watermarked document is public (readable to anyone, almost indistinguishable from the original), while an encrypted document is incomprehensible to others. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ISI-KDD 12, August 12, 2012, Beijing, China. Copyright 2012 ACM /12/08. In this work, the prime focus is on recovery of a sequence such as text from partial information (watermark). A document is modeled by a sequence of alphanumeric characters, and recovery is limited to recovering the original character sequence (but not font, style, etc.). We propose an algorithm to counter all kinds of sequence tampering such as substitution, insertion, deletion or a combination of all. Even though used for text sequences, the idea proposed here is equally applicable to any other sequence. 2. WATERMARK EMBEDDING AND EXTRACTION Before we proceed to tamper detection and recovery, the document watermarking scheme used is described in brief. Since authentication is the objective, the watermark used is fragile; even slight modification will result in extraction error. Embedding involves character segmentation and recognition, creating the watermark for self-embedding, and actual embedding using boundary pixel flipping. 2.1 Character Recognition Our implementation is limited to document images that use some standard font such as Times New Roman, and have only upper case 26 alphabets, lower case 26 alphabets, numerals from 0 to 9, and 2 punctuation marks, making a total of 64 characters. (The scheme may be extended to include all ASCII characters.) Each character is segmented using a standard connected component segmentation. Each character is then recognized using Fourier descriptors (FD) [1,14]. Boundary points of each character are extracted, expressed as a sequence of complex numbers, and a 512-point discrete Fourier transform (DFT) is taken to obtain the FD. Even though FDs are invariant to rotation and translation, they are sensitive to boundary modifications. Since the character boundary is modified by the watermarking process (described later), the FD after watermarking will change. However, low frequency FDs determine the global shape and are relatively less affected. Therefore, we consider only first 21 lower order FDs (DFT coefficients for k = 10 to 10). For the standard fonts and 64 characters, a table of 21 FDs is pre-computed and stored. The table entry closest (mean absolute error) to the obtained FD denotes the character. 2.2 Self-embedding In order to be able to recover tampered characters, self-embedding [2,5-7,11-13] is used, where the watermark is a copy of the cover medium itself. In this work, one unit of watermark is the ASCII code representing a character, which is embedded in one unit of the cover medium, the binary image of a segmented character. However, embedding the code of a character onto itself does not allow its recovery if tampered. Therefore, the code of a character i is embedded on another character (i + c) mod n, where c is a cyclic shift known to the encoder and the decoder (all examples use c = 5), and n is the length of the text sequence. Schemes besides cyclic shift may be found in [13]. Some prior document recovery works use document as a color image [2] which poses less of a challenge. Other works on binary document with character-based [13] or word-based [5] self-embedding used only recovery bits (code of a character or word) as watermark.

2 Consequently these schemes had difficulty against recovery of insertion and deletion tampering. In this work we propose use of authentication bits with position information (counter) to recover any kind of tampering. The authentication bits are encrypted version of a counter denoting the sequence number of a character. For each character, 10 bits are used as the watermark data, where the first 3 bits are authentication bits obtained from a 3 bit counter. Consecutive characters will have (decrypted) counter value 0,1,2,3,4,5,6,7,0,1,2,3, etc. The remaining 7 recovery bits are the ASCII code of a character. 2.3 Watermark Embedding For binary images, pixel values are either black (0) or white (1). Therefore, to embed watermark data, one has to flip pixels from black to white or vice versa. If the pixels to be used for flipping are not selected appropriately, image distortion will be highly perceptible. Low et al. [8-10] proposed a robust data hiding scheme which embeds data in formatted text documents by changing line, word, or character spacing by an indiscernible amount. Wu et al. [16] proposed a data hiding in the binary image using human perception. Distortion that can occur due to flipping of every pixel was measured. Based upon the distortion score, suitable pixels were then selected for flipping. Another scheme of embedding data in 8-connected boundary pixel of a character has been proposed by Mei et al. [15]. Pairs of five-pixel long boundary patterns were identified for pixel embedding data. The use of a suitable distortion model is necessary to minimize the visual distortion in the watermarked images, because minor modification of the pixels can be perceptible since the pixels are either black or white. A perceptual model based on the curvature weighted distance difference (CWDD) measure between two contour segments has been proposed by Ho et al. [3,4], in which the amount of intrusion or protrusion of the boundary due to flipping of a boundary pixel is measured. The model is found to be highly correlated with human perception and particularly useful for sharply contrasted binary images such as text, drawing and signature images. In this work the non-blind watermarking scheme of [3] is modified to a blind scheme. Bounding box of each character is identified. Pixels in the bounding box are randomly divided into 10 groups. This random grouping is based upon a seed key, which is shared only between the watermark encoder and the watermark decoder. For each group, a flippable pixel having the minimum CWDD value is identified. During embedding, this flippable pixel is flipped if necessary to make the group parity equal to the watermark bit. Figure 1 shows an example of a watermarked document. The 10-bit watermark of character S is embedded into the sixth character l, the watermark of the second character i is embedded into the seventh character P, and so on. The watermark image is subjectively indistinguishable from the original. te that since our implementation uses connected component segmentation, dots of i/j appear as separate characters which are then eliminated from further consideration based on their small area. Thus the watermarked (and subsequent) documents in our examples do not have these dots. It is of course possible to put back the dots in their original positions for aesthetic purpose. 2.4 Watermark Extraction The extraction process involves segmentation of characters, determining the bounding box, random grouping of pixels, and determining the parity of each group to obtain 10 bit watermark from each character image. 3. TAMPER DETECTION Tampering of text document by editing attack is classified by us into substitution of character(s) within a document, insertion of character(s), and deletion of character(s) from the document. te that substitution is relatively easy to counter since the sequence length is not altered. Substitution is considered a separate class and not deletion followed by insertion so that, locally, these three classes of editing attack will be mutually exclusive. The extracted watermark sequence is reverse shifted using c and its counter value (first 3 bits) is separated. Example 1: Let the original sequence be: Eat less and stay healthy (1) In absence of any tampering, the extracted and decrypted counter values will be consecutive: 0,1,2,3,4,5,6,7,0,1,2,3,4,5,6,7,0,1,2,3,4. If the tampering involves introducing new characters (substitution or insertion), the counter will have a random value highly unlikely to be consecutive. This is true even if the character is cut-and-pasted from another place of the watermarked document itself. A segment table is created from the counter sequence as follows. A segment is defined as a sequence of consecutive values, and is characterized by its start value start[i], end value end[i], and length l[i]. A segment i is labeled tampered if l[i] is less than a threshold, taken as 2 in this work. The segment table for the above example is i start end length tampered no since all 21 values are consecutive. 3.1 Detecting Deletion Example 2: Consider the tampered sequence from (1) to be Eat sand stay healthy where letters les have been deleted. The counter values are 0,1,2,6,7,0,1,2,3,4,5,6,7,0,1,2,3,4. The segment table is i start end length tampered no Figure 1. Watermark example: original (top), watermarked (middle), flipped pixel locations (bottom).

3 showing two non-tampered segments. Thus, deletion is detected when two adjacent segments i and i+1 are non-tampered, and the difference (start[i + 1] end[i]) mod 8 1 (since 3-bit counter is used) denotes the number of deleted characters. 3.2 Detecting Insertion Example 3: Consider another tampering of (1) as follows. Eat less and cant stay healthy Four characters cant (can t) have been inserted. The counter values are now 0,1,2,3,4,5,6,7,0,1,3,1,5,0,2,3,4,5,6,7,0,1,2,3,4, where the random values from the inserted characters are shown in bold. The resulting segment table is no yes yes yes yes no start[j] = (end[i] + s + 1) mod 8 where s = l[i + 1] + l[i + 2] + + l [j 1] is the number of substituted characters between them. Figure 2 shows the flowchart of the detection algorithm. While it is capable of detecting multiple occurrences of deletion, insertion, substitution, and a mixture of them, it is conceivable that some tampering patterns (such as when two consecutive tamper locations are separated by 0 or 1 character) will be detected but cannot be classified into these 3 classes. Such cases are shown as failure which indicates that even though we are able to detect these, we are unable to recover these segments. Start for all i, if l[i] < threshold Yes Mark i as tampered t tampered showing all inserted characters as tampered segments. Further, the start value after these tampered segments is consecutive to the end value before these segments. Thus, insertion is detected when two non-tampered segments i and j separated by tampered segment(s) satisfy start[j] = (end[i] + 1) mod 8. All in between characters are detected as extraneous. Choose 2 adjacent non-tampered segments: i and j j=i+1 Mark deletion of start [i+1] - end [i] - 1 characters between segments i and i Detecting Substitution Example 4: Lastly, consider a substitution of (1): Eat more and stay healthy Four characters less are substituted by four new characters more. The counter values are now 0,1,2,1,7,5,4,7,0,1,2,3,4,5,6,7,0,1,2,3,4, where the values from the substituted characters are shown in bold. The resulting segment table is yes yes yes yes no if (start[j]) = (end[i] + 1) mod 8 s=l[i+1] + l[i+2] + + l[j-1] if (start[j]) = (end[i] + s +1) mod 8 Detection only Yes Yes Mark all segments i+1, i+2,.., j-1 as substitution Mark all segments between i and j i.e. i+1, i+2,, j-1 as insertion showing all substituted characters as tampered segments. Further, the start value after these tampered segments is 4 more than the end value before these segments, where 4 characters have been substituted. Thus, substitution is detected when two non-tampered segments i and j separated by tampered segment(s) satisfy Repeat for the next 2 adjacent non-tampered segments l[j] and l[k] Figure 2. Flowchart of the tamper detection algorithm.

4 4. RECOVERY After extraction, the watermark recovery bits (7 bits) are retrieved, and similar to the counter array, a character array b is extracted from the watermarks. For a shift of c = 5, for example 1 the array b is: althyeatlessandstayhe 4.1 Recovering Insertion Recovery from insertions is performed first. All inserted characters are dropped. For example 3, the array b is althyeatleih 3ssandstayhe where the random watermark characters obtained from the inserted character images cant are highlighted in bold, and denotes a non- ASCII code. The segment table is updated by removing the inserted segments and concatenating the adjacent non-tampered segments. In the end, after all tampering has been treated, an inverse circular shift will produce the original character sequence (1). 4.2 Recovering Deletion Deletion is addressed next. A special character * is added to replace the deleted characters. Example 2 results in the following character array altatlessandstayhe which, after replacement of deletion, will become: alt***atlessandstayhe In the end, after inverse shift, the character array b will be: *atlessandstayhealt** The segment table is updated by introducing the deleted characters (fourth, fifth and sixth positions) and a label deletion with them. This changes the received text from Eat sand stay healthy to Eat ***sand stay healthy. The * characters in b above are then replaced by the corresponding letters from this updated received text to obtain Eatlessandstayhealthy which indeed is the original sequence (1). 4.3 Recovering Substitution After treating deletion, all substituted characters are replaced by a special character # and the corresponding segments in the segment table are labeled substitution. For example 4 we obtain altu oytlessandstayhe where the watermark characters obtained from the substituted letters more are highlighted in bold. After replacement it is which after inverse shift gives: alt####tlessandstayhe ##tlessandstayhealt## Similar to deletion case, `# is replaced from the updated received text to obtain the original sequence (1): Eatlessandstayhealthy Figure 3. Flowchart of the recovery algorithm. Figure 3 shows the complete flowchart of the recovery algorithm. This algorithm works for any number of tampering of any class, so long as two tampered locations have more than 1 character gap. te that the counter repeats after 8 characters, thus creating an ambiguity as to the length of tampered segments modulo 8. If longer tampering is expected, the maximum counter value should be increased. 4.4 Complete Examples Example 5: We now consider a case of multiple tampering of (1) including all classes: Eat sand to stay wealthy Three characters les are deleted at the early part, two characters to are inserted at the middle part, and a character h is substituted by another character w near the end of this text sequence. The counter values obtained are 0,1,2,6,7,0,1,4,0,2,3,4,5,3,7,0,1,2,3,4, where the values from the inserted and substituted characters are shown in bold. The resulting segment table is as follows.

5 no yes yes no yes no The detection algorithm starts by choosing first two non-tampered segments, 1 and 2. Since they are adjacent, a deletion of start[2] end[1] 1 = 3 characters is detected. Next, two non-tampered segments 2 and 5 are considered. Since they are not adjacent, it is not deletion. However, start[5] = end[2] + 1, so an insertion of segments 3 and 4 (eighth and ninth characters, to) is detected. Next pair of nontampered segments are 5 and 7. They are neither adjacent nor satisfy the insertion condition. However, start[7] = end[5] + s + 1, where s = l[6] = 1. Therefore, a substitution is detected at segment 6 (fourteenth character, w). The character array b obtained in this case is altatlebxssanpstayhe The recovery algorithm begins by removing the inserted segments 3 and 4, resulting in: altatlessanpstayhe The updated segment table after removing the inserted segments 3, 4 and merging segments 2,5 is no yes no and the updated received text is: Eat sand stay wealthy Since there is no more insertion, deletion of 3 characters between segments 1 and 2 is treated next. The result is: alt***atlessanpstayhe The segment table is updated by introducing the deleted characters, deletion no yes no Figure 4. Recovery example: watermarked (top), tampered (middle), recovered (bottom). and the updated received text is: Eat ***sand stay wealthy (2) Since there is no more deletion, substitution of updated segment 4 is replaced by # to obtain: alt***atlessan#stayhe Since this is the last step, there is no need to update the segment table, except appropriate segments may be labeled substitution. Since substitution does not change the length of a sequence, there is no need to update the received text either. Inverse shift of the b array gives the final result, *atlessan#stayhealt** where 3 characters of segment 2 are labeled deletion and 1 character of segment 4 is labeled substitution. A successful recovery is achieved by replacing these unknown characters from the updated received text (2): Eatlessandstayhealthy The code developed uses the same font library (known from FD based recognition), and generates a recovered document image that is almost identical to the original document except spaces. Figure 4 shows another example of the final result of recovery of a tampered document. Such results are easily extended to full-length documents. 5. CONCLUSION An application is developed using document watermark for detection and recovery of editing based tampering. Unlike its predecessors, the proposed scheme uses both recovery bits and authentication bits with position information in the watermark to successfully recover a sequence of characters (text) even against insertion or deletion. The tamper detection and recovery algorithms proposed here are applicable beyond text to any sequence recovery. The presented scheme is easily

6 extendible to larger character set, larger font set, and longer tampering. With appropriate character recognition, it is extendible to handwritten documents as well. Our future effort is towards dealing with editing attacks that the present scheme is unable to recover, such as two consecutive tampering or word/phrase cut-and-pasting. It should be mentioned that the proposed recovery strategy fails for certain tamper patterns, or when too many characters are tampered. Obviously, if both character i and character i+c are tampered, then character i can not be recovered. These issues are discussed and the probability of such restoration failure (both theoretical and simulated) for random substitution attack is presented in [13]. 6. REFERENCES [1] Cabrelli, C.A. and Molter, U.M Automatic representation of binary images. IEEE Trans. PAMI, 12(12) (Dec. 1990). [2] Cheddad, A., Condell, J., Curran, K., and McKevitt, P A secure and improved self-embedding algorithm to combat digital document forgery. Signal Proc., 89, [3] Ho, A.T.S., Puhan, N.B., Makur, A., Marziliano, P., and Guan, Y.L Imperceptible data embedding in sharply-contrasted binary images. Proc. Intl. Conf. ICARCV, 2, [4] Ho, A.T.S., Puhan, N.B., Marziliano, P., Makur, A., and Guan, Y.L Perception based binary image watermarking. Proc. IEEE Intl. Symp. Circuits Systems, 2, [5] Khen, T.V. and Makur, A A word based self-embedding scheme for document watermark. Proc. IEEE Region 10 Intl Conf (TENCON). [6] Kostopoulos, I., Gilani, S.A.M., and Skodras, A.N Colour image authentication based on a self-embedding technique. Proc. Intl. Conf. DSP, 2, [7] Lan, T.-H. and Tewfik, A.H Fraud detection and self embedding. Proc. ACM Multimedia, part 2, [8] Low, S.H. and Maxemchuck, N.F Performance comparison of two text marking methods. IEEE Jour. Selected Area Comm, 16(4). [9] Low, S.H., Maxemchuck, N.F., Brassil, J.T., and O Gorman, L Document marking and identification using both line and word shifting. Proc. Conf. Infocom, IEEE Comp. Soc. Press. [10] Low, S.H., Maxemchuck, N.F., and Lapone, A.M Document identification for copyright protection using centroid detection. IEEE Trans. Comm., 46(3), [11] Luo, H., Chu, S.-C., and Lu, Z.-M Self embedding watermarking using halftoning technique. Circuits Syst. Signal Process., 27, [12] Luo, H., Lu, Z.-M., Chu, S.-C., and Pan, J.-S Self embedding watermarking scheme using halftone image. IEICE Trans. Inf. & Syst., E91-D(1), [13] Makur, A Self-embedding and restoration algorithms for document watermark. Proc. ICASSP, 2, [14] Man, G.M.T. and Poon, J.C.H An enhanced approach to character recognition by Fourier descriptor. Proc. Conf. ICCS/ISITA, Singapore. [15] Mei, Q.G., Wong, E.K., and Memon, N.D Data hiding in binary text documents. Proc. SPIE Conf. Security Watermarking Multimedia Contents III, 4314, [16] Wu, M., Tang, E., and Liu, B Data hiding in digital binary image. Proc. IEEE Intl. Conf. Multimedia Expo.