Quality based JPEG Steganography using balanced Embedding Technique

Transcription

1 Second International Conference on Emerging Trends in Engineering and Technology, ICETET-09 Quality based JPEG Steganography using balanced Embedding Technique Mohammed Ishaque Department of Computer Science King Khaled University Abha, Saudi Arabia Dr.Syed Abdul Sattar Dean Royal Institute of Technology and Science, Hyderabad, India Abstract A high-performance JPEG steganography should be secure enough to resist modern steganalysis. In this paper, we propose a high-performance JPEG steganographic method. The proposed method adopts the complementary embedding strategy to avoid the detections of several statistical attacks. To show the effectiveness of the proposed method, several statistical attacks are simulated and used to detect the stego-images created by the proposed method. Several famous JPEG steganographic algorithms are also simulated for comparisons with the proposed method. Experimental results show that the proposed steganographic method has superior performance both in capacity and security, and is practical for the application of secret communication.. Introduction The success of the Internet facilitates communications of people, but also enables illegal users to access data transmitted on the Internet. To protect the important data from being illegally accessed, various modern cryptosystems [] can be used to encrypt the content of these data prior to their transmissions. However, the encrypted data exists in a meaningless form and may attract the intention of the interceptors to break the secret codes. Steganographic methods can be used to make up this drawback. Steganographic methods [2 24] and various watermarking schemes[25 28] are applications of information hiding techniques [29]. Steganographic methods hide the secret information in the cover carrier so that the existence of the embedded information is undetectable. The cover carrier can be many kinds of digital media such as text, image, audio, and video. Because of the insensitivity of the human visual system, digital images have been widely used as cover carriers in most steganographic schemes, and are especially referred to as image hiding techniques [2,3,5]. Each image hiding system consists of an embedding process and an extraction process. An innocuous-looking original image is used as the cover-image to conceal the secret data. The secret data are embedded into the cover-image by modifying the cover-image to form a stego-image. According to Kerckhoffs principle [30], the embedding algorithm is supposed to be known to the public. Therefore, the embedding process may use an embedding key so that only the legal user can successfully extract the embedded data by using the corresponding extraction key in the extraction process. The embedding key and the extraction key are referred to as stego-keys. If they are the same, the image hiding system is symmetric, otherwise asymmetric []. Most image hiding systems use uncompressed images (e.g., BMP) or losslessly compressed images (e.g., GIF) as coverimages. These images potentially contain much visual redundancy so that they can provide large capacity to hide secret data. Many image hiding systems [2 24] have been proposed and several stego-products have been developed based on lossless image formats (e.g., EzStego [9]). For reducing transmission bandwidth and storing space, the JPEG image is currently the most common format used on the Internet. Several image /09 $ IEEE 25

2 hiding techniques for JPEG images have been proposed, such as J-Steg [8], F5 [6], and OutGuess [7]. They are usually called the JPEG steganography or the JPEG steganographic methods which use the DCT coefficients to embed the secret bits, and will be mentioned alternately in the rest of this paper. As we know, the JPEG compression is based on the discrete cosine transform (DCT), and reduces the visual redundancy to achieve good compression performance. Therefore, the embedding capacity provided by JPEG steganography is relatively smaller than those provided by the other steganographic methods. In this paper, our discussions focus on the JPEG steganography. Generally, there are potentially two basic requirements for a secure image hiding system. First, the secret data embedded in the stego-image should be perceptually invisible. Secondly, the receiver can exactly recover the original data without the knowledge of the cover-image. These requirements can be easily achieved by LSB insertion methods that embed the secret data in the cover-image by directly replacing the least significant bit (LSB) in spatial domain or frequency domain. For example, the JPEG steganographic tool J-Steg embeds the secret data by sequentially flipping the LSB of the quantized DCT coefficients (except 0s and s) without causing detectable artificial distortion. Unfortunately, modern steganalysis tries to discover hidden data by examining the statistical properties of stego-images. Therefore, many existing steganographic techniques seem to be insecure when modern steganalysis is employed to test their security. Westfeld and Pfitzmann [3] proposed a steganalytic method based on statistical analysis of Pairs of Values (PoVs) that are exchanged during message embedding. They observed that the embedding function replacing LSBs transforms PoVs into each other which only differ in the LSB. If the secret bits used for replacing the LSBs are equally distributed, the frequencies of both values of each PoV become equal. Based on this observation, they proposed a statistical method to automatically calculate the similarity of PoVs of the stego-image. Their method is generally known as the chi-square attack and provides very reliable results when the LSB placement is known (e.g., the sequential embedding). Therefore, the data embedded by the J-Steg can be detected by chi-square attack. Actually, the chisquare attack can be slightly modified to successfully detect randomly scattered data by applying the same idea to smaller portions of the stego-image [7]. The modified chi-square attack is especially referred to as the extended chi-square attack in the rest of this paper. We also briefly use the chi-square family attack to represent both attacks together. To provide a secure and high-capacity JPEG steganography, Westfeld [6] proposed the F5 algorithm in 200. Instead of replacing the LSBs of the quantized DCT coefficients with the secret bits, the absolute value of the coefficient is decreased by. Besides, the F5 algorithm randomly chooses DCT coefficients to embed the secret bits. Combining these two strategies, the F5 algorithm successfully defends both the chi-square and the extended chi-square attacks. In the same year, Provos [7] also proposed the OutGuess steganographic algorithm to counter the chi-square family attack. The OutGuess uses two passes to achieve the embedding mission. In the first pass, the secret bits are embedded in the LSBs of the quantized DCT coefficients (skipping 0s and s) along a random walk. In the second pass, the histogram of the stego-image is adjusted to match that of the cover-image. Because the histogram of the final stego-image is similar to that of the cover-image, the stego-image created by the OutGuess algorithm can avoid the detection of the chi-square family attack. In this paper, we propose a secure and high-capacity JPEG steganography. Instead of flipping the LSBs of the DCT coefficients, the secret bits are embedded in the cover-image by subtracting one from or adding one to the non-zero DCT coefficients. Therefore, the proposed steganographic method cannot be detected by both the chi-square and the extended chi-square attacks. Moreover, a complementary strategy is also included in the proposed method to defend the S and S2 attacks. 2. Review of JPEG compression Because the proposed steganographic method is a JPEGbased steganography, we briefly describe the JPEG compression technique in this section. Original image FDCT Quantization encoding Compressed code Fig.. The block diagram of the JPEG compression process. JPEG is an international standard for continuous-tone still image compression which has been approved by International Standard Organization (ISO) under the denomination of International Standard-098 (IS-098) [33]. The JPEG compression is based on the DCT and allows substantial compression to be achieved while producing a reconstructed image with high visual fidelity. Fig. shows the JPEG encoding process which comprises three major steps: forward DCT (FDCT), quantization, and entropy encoding. The input image is first divided into 8 8 non-overlapping blocks, and each block is transformed by the FDCT into a set of 64 DCT coefficients. These coefficients are then quantized using a quantization table with 64 entries. The quantized results are all integers and defined as the division of each DCT coefficient by its corresponding quantization value, and rounding to the nearest integer. The quantization step is lossy because of the rounding error. The quantized coefficients are then passed to the entropy encoding step to form the compressed code. One of two tables must be provided in this step according to different entropy coding schemes. If Huffman encoding is used, a Huffman table must be provided. If arithmetic encoding is used, an arithmetic coding conditioning table must be provided. It should be noted that the entropy encoding step is lossless. The JPEG decoding process is shown in Fig. 2, and also comprises three major steps: entropy decoding, dequantization, and inverse DCT (IDCT). Each step in decoding process performs essentially the inverse of its corresponding step in encoding procedure. The entropy decoding step decodes the compressed code to the quantized DCT coefficients. The dequanti- 26

3 Compressed code decoding Dequantization IDCT Reconstructed image Fig. 2. The block diagram of the JPEG decompression process. zation step then converts each quantized DCT coefficient to its approximate value by multiplying with its quantization value. DCT is then used to convert the dequantized coefficients to their spatial value. As mentioned above, the entropy encoding step in the JPEG encoding process is lossless. It means that if the secret bits are embedded in the quantized DCT coefficients, they will not be destroyed by the follow-up encoding steps. Most of the JPEG steganographic methods (such as J-Steg, F5, OutGuess, etc.) adopt this strategy. In the next section, we propose a steganographic method which also uses the quantized DCT coefficients to embed the secret bits. 3. The proposed JPEG steganography In this section, a secure and high-capacity JPEG steganography is proposed. As mentioned previously, the S family attack obtains the approximate cover-image to break the F5 and Out- Guess schemes. To counter the detection of such attack, the proposed JPEG steganographic method adopts a complementary strategy to reduce the loss of statistical property of the cover-image. This purpose is achieved by dividing the quantized DCT coefficients and the secret bits into two parts according to a predefined partition ratio. The two parts of DCT coefficients are used to embed the corresponding parts of secret bits with different embedding algorithms. Specifically, the secret bits are embedded by subtracting one from one part of coefficients, and adding one to the other part of coefficients. Specially, we call our proposed embedding algorithm the complementary embedding. Moreover, because the proposed embedding algorithm is not an LSB insertion one, the proposed JPEG steganographic method can also resist the chi-square family attack. The proposed stegnographic method comprises an embedding process and an extraction process. The details of the embedding and extraction process are described in the following subsections. 3.. Embedding process The proposed embedding process is integrated with JPEG encoding process. Fig. 3 shows the block diagram of the embedding process. The raw data of the cover-image is first transformed by DCT. The DCT coefficients are then quantized and rounded to the nearest integers. A stego-key, which provides the major security of the embedding algorithm, is then used to permute the DCT coefficients. The permuted coefficients are then divided into two parts according to a predefined separation ratio which serves an important parameter to reduce the loss of statistical property of the cover-image resulted from the follow-up secret-bits embedding process. On the other hand, the original message is encrypted to form the secret bits by using a crypto-key. The secret bits are also divided into two parts according to the same separation ratio. Each part of secret bits is embedded in its corresponding part of the permuted non-zero DCT coefficients by using the proposed complementary embedding algorithm. The two parts of the modified coefficients are then combined, depermutated, and entropy encoded to generate a JPEG stegoimage. The details of the proposed embedding process are as follows. Step : Transform the raw data of the cover-image (e.g., the luminance component) into DCT coefficients. Step 2: Quantize the DCT coefficients, and round the quantized coefficients to the nearest integers D. Step 3: Use a stego-key K to permute the quantized coefficients. That is, Q = PERM K (D), () where PERM( ) is a key-dependent permutation function, and Q denotes the permuted coefficient sequence. Step 4: Divide Q into two parts, Q and Q 2, according to a predefined separation ratio x. Step 5: Use a crypto-key K 2 to encrypt the original message O, and obtain the secret-bit sequence S. That is, S = ENC K2 (O ), (2) where ENC( ) is a cryptographic encryption function. Step 6: Divide S into two parts, S and S 2, according to the separation ratio x. Step 7: Let L denote the length of S, concatenate L and S to form the secret message M. That is, M = L S, (3) where denotes the concatenation operation. Step 8: Let L 2 denote the length of S2, concatenate L 2 and S 2 to form the secret message M 2. That is, M 2 = L 2 S 2. (4) Step 9: Embed M into the non-zero coefficients of Q according to the proposed E embedding algorithm given in Table. Note that, a secret parameter fi is also used in the embedding algorithm to make it more difficult for a statistical attack to obtain the original statistical property of the cover-image from a stego-image. Step 0: Embed M 2 into the non-zero coefficients ofq 2 according to the proposed embedding E2 algorithm given in Table 2. Step : Combine the modified Q and Q 2 to form a single coefficient sequence Qi Step 2: Use the stego-key K sequence Q i. That is, where DEPERM( ) is a key-dependent de-permutation function, and D i denotes the de-permuted DCT coefficients. Step 3: Compress the D i using entropy encoding to obtain the JPEG stego-image.. 27

4 Stego-key Cover image FDCT quantization permutation separation Crypto-key Original message Cryptographic encryption separation embedding JPEG stego-image encoding de-permutation combination Fig. 3. The block diagram of the proposed embedding process. Table The proposed E embedding algorithm Algorithm: E embedding Input: B : the bit sequence to be embedded C: a coefficient sequence fi: adjustment parameter O utput : C i : the modified version of C Begin /* E embedding */ for i = to length(b ) if C(i) > 0 and odd(c(i)) = /* C(i) is odd */ if B(i) = 0 and C(i) = 0 2 and C(i) = 0 elseif B(i) = elseif C(i) > 0 and odd(c(i)) = 0 /* C(i) is even */ if B(i) = elseif C(i) < 0 and odd(c(i)) = if B(i) = elseif C(i) < 0 and odd(c(i)) = 0 if B(i) = 0 elseif C(i) = endfor for i = to fi length(b ) if C i (i) = 2 C i (i) = endfor end /* E embedding */ Table 2 The proposed E2 embedding algorithm Algorithm: E2 embedding Input: B : the bit sequence to be embedded C: a coefficient sequence Output: C i : the modified version of C Begin /* E2 embedding */ for i = to length(b ) if C(i) > 0 and odd(c(i)) = /* C(i) is odd */ if B(i) = + elseif C(i) > 0 and odd(c(i)) = 0 /* C(i) is even */ if B(i) = 0 + elseif B(i) = elseif C(i) < 0 and odd(c(i)) = if B(i) = 0 and C(i) + = and C(i) + = 0 + elseif B(i) = elseif C(i) < 0 and odd(c(i)) = 0 ifb(i) = + endfor end /* E2 embedding */ It should be noted that, in most cases, we do not use all the coefficients of Q and Q 2 to embed S and S 2. Therefore, in Steps 7 and 8, L and L 2 are embedded prior to the embedding of S and S 2 so that they can be extracted first to inform the extraction algorithm to extract the exact size of S and S 2. 28

5 3.2. Extraction process The details of the proposed extraction process are as follows. Step : -decode the stego-image to recover the quantized DCT coefficients D. Step 2: Use the shared stego-key K to permute D. That is, Q = PERM K (D), (6) where PERM( ) is a key-dependent permutation function, and Q denotes the permuted coefficient sequence. Step 3: Divide Q into two parts, Q and Q 2, according to a shared separation ratio x. Step 4: Let l denote the bit length of L and L 2. For i = to l, extract the ith bit of L from ith coefficient of Q according to the following equation: Q (i) > 0 and odd (Q (i)) = 0, L (i) = Q (i) < 0 and odd (Q (i)) =, (7) Q (i) > 0 and odd (Q if (i)) =, Q (i) < 0 and odd (Q (i)) = 0, where odd(x) determines the property of x. If x is odd, odd(x) returns the value, otherwise 0. Step 5: For i = to L, extract the ith bit of the secret-bit sequence S from (l +i)th coefficient of Q according to the following equation: Q (k) > 0 and odd (Q (k)) = 0, S (i) = Q (k) < 0 and odd (Q (k)) =, Q (k) > 0 and odd (Q if (k)) =, Q (k) < 0 and odd (Q (k)) = 0, where k = l + i. Step 6: For i = to l, extract the ith bit of L 2 from ith coefficient of Q 2 according to the following equation: Q2 (i) > 0 and odd (Q 2 (i)) =, L 2 (i) = Q 2 (i) < 0 and odd (Q 2 (i)) = 0, (9) Q2 (i) > 0 and odd (Q if 2 (i)) = 0, Q 2 (i) < 0 and odd (Q 2 (i)) =. (8) Step 7: For i = to L 2, extract the ith bit of the secret-bit sequence S 2 from (l +i)th coefficient of Q 2 according to the following equation: Q2 (k) > 0 and odd (Q 2 (k)) =, S 2 (i) = Q 2 (k) < 0 and odd (Q 2 (k)) = 0, (0) Q2 (k) > 0 and odd (Q if 2 (k)) = 0, Q 2 (k) < 0 and odd (Q 2 (k)) =, where k = l + i. Step 8: Combine S and S 2 to form a single secret-bit sequence S. Step 9: Use a shared crypto-key K 2 to decrypt S, and obtain the original message O. That is, O = DEC K2 (S), () where DEC( ) is a cryptographic decryption function. 4. Experimental results Several experiments have been done to examine the performance of the proposed embedding method. Table 3 Comparison of capacity (in bits) for various embedding Algorithms Test image Embedding algorithm The proposed J-Steg F5 OutGuess Barb Boat F Goldhill Lena Mandrill Peppers Tank Tiffany Zelda JPEG stego-image decoding permutation separation Crypto-key Stego-key Original message Cryptographic decryption combination extraction Fig. 4. The block diagram of the proposed extraction process. 29

6 Fig. 5. The test images used in our experiments: (a) Barb, (b) Boat, (c) F6, (d) Goldhill, (e) Lena, (f) Mandrill, (g) Peppers, (h) Tank, (i) Tiffany, and (j) Zelda Imperceptibility test Table 4 The PSNR values (in db) of the stego-images created by the proposed method using various embedding rates Test image Embedding rate 20% 40% 60% 80% 00% Boat F Lena Peppers Tiffany Zelda Table 5 Comparison of PSNR values (in db) of the stego-images created by various embedding algorithms Test image Embedding algorithm The proposed J-Steg F5 OutGuess Boat F Lena Peppers Tiffany Discussions The S and S2 attacks are good statistical attacks. Although they were originally designed for breaking F5 and OutGuess, they can also be used to break the other JPEG steganographic methods if the embedding strategy of any steganographic method matches the detection strategy of S or S2. In brief, the S attack uses a method to estimate the original image from the stego-image, and assumes that, after embedding, the number of the 0 and the absolute-value- DCT coefficients in the stego-images created by F5 will be changed drastically. Based on this property, the S attack can measure the amount of the embedded secret bits using the relation between the stego-images and the estimated original image. Because OutGuess preserves the property of the DCT coefficients of the stego-images, if we use S attack to detect the stegoimages created by the OutGuess algorithm, we will obtain incorrect detection results as given in Table 7. Similarly, because the proposed method uses the strategy of complementary embedding to maintain the property of DCT coefficients, the S attack cannot correctly detect the number of the embedded secret bits as shown in Fig. 8. This is why the proposed steganographic method can resist the S attack. 6. Conclusions In this paper,we have proposed a quality based JPEG steganographic method. The proposed method adopts the balanced complementary embedding strategy to reduce the loss of statistical property of the stego-image in spatial domain. In such a manner, the proposed steganographic method can resist the S family attack. Moreover, the proposed method does not adopt the LSB replacement strategy so that it can also avoid the detection of the chi-square family attack. To show the effectiveness of the proposed method, the chisquare, extended chi-square, S and S2 attacks were simulated, and used to detect the stego-images created by the proposed method. The J-Steg, F5, and OutGuess steganographic algorithms were also simulated and used to create the stego-images in the same experimental conditions. Experimental results show that the proposed steganographic method has the following advantages: () The capacity provided by the proposed method is larger than those provided by J-Steg, F5, and OutGuess steganographic methods. (2) The proposed method has excellent performance against the chi-square family attack. tion of the S family attack. With the above-mentioned advantages, the proposed steganographic method is practical for the application of covert communication. 220

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