Wavelet transform modulus maxima‐based robust logo watermarking

IET Image ProcessingVolume 14, Issue 4 p. 697-708 Research ArticleFree Access Wavelet transform modulus maxima-based robust logo watermarking Mohammad Barr, Corresponding Author Mohammad Barr m.abarr@nbu.edu.sa Department of Electrical Engineering, Northern Border University, Arar, Saudi ArabiaSearch for more papers by this authorCristian Serdean, Cristian Serdean School of Engineering and Sustainable Development, De Montfort University, Leicester, UKSearch for more papers by this author Mohammad Barr, Corresponding Author Mohammad Barr m.abarr@nbu.edu.sa Department of Electrical Engineering, Northern Border University, Arar, Saudi ArabiaSearch for more papers by this authorCristian Serdean, Cristian Serdean School of Engineering and Sustainable Development, De Montfort University, Leicester, UKSearch for more papers by this author First published: 18 February 2020 https://doi.org/10.1049/iet-ipr.2018.5868Citations: 3AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Digital image watermarking is used to protect the copyright of digital images. In this study, a novel blind logo image watermarking technique for RGB images is proposed. The proposed technique exploits the error correction capabilities of the human visual system. It embeds two different watermarks in the wavelet/multiwavelet domains. The two watermarks are embedded in different sub-bands, are orthogonal, and serve different purposes. One is a high capacity multi-bit watermark used to embed the logo, and the other is a 1-bit watermark which is used for the detection and reversal of geometrical attacks. The two watermarks are both embedded using a spread spectrum approach, based on a pseudo-random noise sequence and a unique secret key. Robustness against geometric attacks such as rotation, scaling, and translation (RST) is achieved by embedding the 1-bit watermark in the wavelet transform modulus maximacoefficients of the wavelet transform. The experimental results show that the proposed watermarking technique has better distortion parameter detection capabilities and compares favourably against existing techniques in terms of robustness against geometrical attacks such as RST. 1 Introduction Digital watermarking is a valuable technique which enables the hiding or embedding of a signal (usually containing ownership information) into another signal (usually an image or video content) [1]. Spread spectrum techniques [2, 3] and wavelet transforms have proved to be very popular in watermarking in spite of their susceptibility to 'desynchronisation' attacks and respectively the lack of shift-invariance of the conventional wavelet transforms. Various techniques have been used over time to address these issues and provide robustness to geometrical and other desynchronisation-type attacks [4, 5]. One technique that addresses the aforementioned limitation of the wavelet (and multiwavelet) transforms [6], is to make use of the dual-tree complex wavelet transform (DTCWT) [7]. In the context of watermarking, DTCWT has several useful features such as approximate shift invariance, low computational complexity, perfect reconstruction, and directional sensitivity [8]. However, watermarking schemes based on DTCWT usually have low embedding capacity [9]. Another possible technique to address this limitation and which avoids the need to rely on a complex transform is to make use of the wavelet transform modulus maxima (WTMM) coefficients of the wavelet transform. The maxima values of the wavelet transform, also known as the WTMM [10, 11], provide interesting insights about an image. For example, they represent edge related information in an image but unlike the standard DWT (d wavelet transform) coefficients which are not translation invariant, the WTMM coefficients are translation invariant. This valuable property is exploited in this paper to fight against geometrical attacks on watermarks. WTMM has been little used so far in digital watermarking. For example, Zhu and Zhu [12], Alghouniemy and Tewfik [13] and Luo et al. [14] are amongst the few authors that employed WTMM in watermarking. It is worth noting that although WTMM has been very little used in digital watermarking, it has been used quite extensively especially for image fusion, in the field of medical imaging [15, 16] and for stereo correspondence matching in stereo vision applications, where it has shown very good promise. The use of DTCWT for watermarking has been first investigated by Loo and Kingsbury [17, 18]. Various techniques have been proposed since then to increase invisibility and robustness and to address the lower capacity of the DTCWT [8, 9, 19], although, beyond cropping and translation, most of these techniques tend to offer limited robustness to other specific geometrical transformations. Other watermarking techniques designed to provide robustness to geometrical attacks based on shift-invariance transform properties, include O'Ruanaidh & Pun's [20] Fourier–Mellin transform (FMT) based watermarking technique. However, the FMT is computationally complex and due to the need to preserve FFT symmetry, offers a low watermark capacity. Amongst other techniques, DWT and singular value decomposition (SVD) were used together with scale-invariant features transform by Ye et al. [21]. Similarly, Fazli & Moeini's discrete cosine transform, SVD, and DWT-based watermarking scheme [22] is another efficient technique. However, they do not take into consideration human visual system (HVS) during watermark embedding, and hence, their method has low capacity. Li and Zhu [23] also used DWT and SVD with Zernike moments in their watermarking scheme. However, like the scheme in [21], their scheme also has disadvantages such as inaccuracy, high computational complexity, and numerical instability at higher order of moments. The main aim of this work is to propose a novel blind logo watermarking technique for RGB images, which is robust to geometrical transforms. The proposed technique exploits the error correction capabilities of the HVS. It embeds two different watermarks in the wavelet and multi-wavelet domains, respectively. The two watermarks are embedded in different sub-bands, are orthogonal, and serve very different purposes. One is a high capacity multi-bit watermark used to embed the logo, and the other is a 1-bit watermark which is used for the detection and reversal of geometrical attacks. The two watermarks are both embedded using a spread spectrum approach, based on a pseudo-random noise (PN) sequence and a unique secret key. Robustness against geometric attacks such as rotation, scaling, and translation (RST) is achieved by embedding the 1-bit watermark in the WTMM coefficients of the wavelet transform. The proposed scheme also looks at how the HVS can be taken into account and adaptively used for RGB images. For example, the human eye has different levels of sensitivity for different colour components and different frequencies [24]. This fact is exploited by embedding watermarks of different strengths in each colour component according to the sensitivity level of the eye to a particular colour component. The experimental results show that the proposed watermarking technique has better distortion parameter detection capabilities and compares favourably against existing techniques in terms of robustness against geometrical attacks such as RST. 2 Wavelet transform modulus maxima In this work, WTMM has been used mainly because of its shift-invariance property. It is the WTMM's shift-invariance property, which standard wavelets and multiwavelets do not have, that enables the proposed watermarking scheme to identify and undo geometrical attacks. Consider an image and let represent a 2D low-pass filter. Let and represent two partial derivative functions of along the horizontal and vertical directions, respectively. The functions and are defined in (1) and (2), respectively [14, 25] as: (1) (2)Let denote the scale of wavelet transform. Then, the partial derivatives of at each scale can be defined as . The wavelet transform at each scale has two components and . These components represent the horizontal and vertical directions of wavelet transform as denoted in (3). (3)The magnitude and the angle of the WTMM can be computed as in (4) and (5), respectively: (4) (5)where . The WTMM coefficients can be identified by applying a suitable threshold to the magnitude Mf of the WTMM and can be represented with the help of a binary image where white and black pixels correspond to zero and large-amplitude (modulus maxima) coefficients, respectively. The angle Af of the WTMM represents the angles at the points where the modulus is non-zero. Fig. 1 shows the vertical detail (LH) sub-bands, horizontal detail (HL) sub-bands, magnitude , phase , and the WTMM images corresponding to one level of decomposition and Haar wavelet, respectively. Fig. 1Open in figure viewerPowerPoint WTMM examples for (a) Lena, (b) Pepper, (c) Barbara images. From left to right: LHsub-band, HL sub-band, WTMM magnitude (Mf), WTMM angle , and WTMM coefficients The WTMM can be calculated using the following steps: Separate the Low–High (LH) and High–Low (HL) parts of the wavelettransform coefficients so that they can be used to calculate theabsolute value (magnitude) and the angle of the WTMM.‏ Compute the magnitude (absolute value) of wavelet transform coefficientsusing (5). Use a threshold value to retain the wavelet transform coefficients with amagnitude larger than the threshold and discard the wavelet transformcoefficients with a magnitude smaller than the threshold. Compute the angle of wavelet transform coefficients using (5). Quantise the angle to the multiple of to force the angle to start from 45degrees. Obtain the quantised angle as it follows: Round the angle values to the nearest integer value. Divide the rounded values by . The quantised angles obtained are used as the direction of the angle. Thereare eight possible directions: 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°. Theseangles correspond to the horizontal (right), diagonal (right, up), vertical (up),diagonal (left, up), horizontal (left), diagonal (left, bottom), vertical (down),and diagonal (right, bottom) directions, respectively. The central pixel and itseight neighbouring pixels are used to determine the direction. The relevantdirection is decided by the value of the angle. Fig. 2 shows some visual examples of the WTMM outputs for RST. Fig. 2Open in figure viewerPowerPoint Visual examples of WTMM outputs for RST (a) WTMM of the original Lena image, (b) Lena watermarked image rotated by(15°), (c) Lena watermarked image scaled by (0.7), (d) Lena watermarked image translated by( + 80, −80) 3 Proposed technique This section presents a detailed description of the proposed robust logo image watermarking technique for colour images. The proposed technique consists of two parts: watermark embedding and watermark detection. Watermark detection itself is a two-stage process. The proposed watermark embedding technique is presented in Section 3.1 while the proposed watermark detection technique is presented in Section 3.2. 3.1 Watermark embedding The key features of the proposed watermark embedding technique are the embedding of two orthogonal watermarks in different DWT sub-bands of the same host image and the use of the shift invariant WTMM for achieving robustness against geometric attacks. The first watermark is a robust 1-bit watermark which is embedded in the low–high (LH) and high–low (HL) sub-bands of the DWT of the original image, while the second watermark is a high capacity multi-bit watermark (a logo image) which is embedded in the high–high (HH) sub-bands of the discrete multiple wavelet transform (DMWT) of the watermarked image already containing the 1-bit watermark. The 1-bit watermark is embedded in the wavelet domain, using the Haar wavelet, while the multi-bit watermark is embedded in the multiwavelet domain using the Cardbal2 multiwavelet. The proposed watermark embedding technique is shown in Fig. 3. As it is shown in Fig. 3, DWT is first applied to the original image and the LH and HL sub-bands are used to calculate the WTMM and embed a 1-bit watermark. Fig. 3Open in figure viewerPowerPoint Overall watermark embedding process The principle behind the WTMM and 1-bit watermark is that WTMM provides the (shift invariant) modulus maxima values so that the 1-bit watermark can be embedded in the relevant shift invariant coefficients. The PN sequence corresponding to the 1-bit WM is generated based on a secret key. The purpose of embedding this 1-bit watermark is to improve the robustness of the proposed scheme against geometric attacks. Once the 1-bit watermark is embedded, the next step is to apply the inverse DWT to obtain the 1-bit watermarked image. Then, the DMWT is applied to the 1-bit watermarked image to access the HH sub-band. The actual logo image which is the multi-bit watermark is then embedded in the HH sub-band with the help of a PN sequence and a secret key. Lastly, inverse DMWT is again applied to obtain the final watermarked image. The motivation behind using two different watermarks is that the 1-bit watermark can be used to determine if the watermarked image has been attacked using a geometric attack, establish what the attack parameters were and undo this attack prior to the extraction of the logo. While spread-spectrum techniques possess many appealing properties [26], their main disadvantage is their sensitivity to any geometric attack that leads to 'desynchronisation' between the resulting image and the generated PN sequence. Therefore, the proposed watermark embedding technique relies on the shift-invariance property of the WTMM to embed a robust 1-bit watermark. The embedding of a 1-bit watermark is solely done as an attack detection mechanism, allowing the proposed watermarking scheme to undo the geometric transformation and 'resynchronise' the image prior to the recovery of the multi-bit logo (watermark). If the 1-bit watermark cannot be instantly recovered, this signals the presence of a geometric attack. In such a case, the geometric attack is first identified and then undone. Then, the proposed algorithm recovers the multi-bit logo. The 1-bit watermark and multi-bit watermark embedding processes are illustrated in more detail in Figs. 4 and 5, respectively. Fig. 4 shows the embedding process of the low capacity 1-bit watermark. Fig. 4Open in figure viewerPowerPoint Proposed 1-bit watermark embedding process Fig. 5Open in figure viewerPowerPoint Proposed multi-bit watermark embedding process After embedding the high capacity watermark, the watermarked image is then split into its three colour components R, G, and B. DWT is applied on each colour component. This is followed by a step in which the LH, and HL sub-bands are extracted from each colour component. At the end of this step, six matrices corresponding to the LH and HL coefficients of each of the three colour components are obtained. Next, WTMM is computed for these components. The WTMM provides the (shift invariant) modulus maxima values so that the 1-bit watermark can be embedded in the relevant shift invariant coefficients. For simplicity, these outputs can be merged together and treated as one large contiguous block (with a higher chip rate) for watermark insertion (and later detection) purposes. The 1-bit watermark is then embedded with respect to the PN sequence and a secret key. Lastly IDWT is applied to each of the colour component to obtain the final watermarked image. From Fig. 5, it can be seen that for the embedding of the multi-bit watermark, the original image is first read. It is then split into its three colour components: Red, Green, and Blue represented by the 'R', 'G', and 'B' blocks, respectively. Next, the HH sub-band is extracted from each of these components. This is followed by a step in which the HH sub-band coefficients of the R, G, and B components are merged into a large contiguous block. This effectively triples the chip rate reported to the size of the image. The logo is then spread over the merged coefficients. This is done with the help of a PN sequence and a secret key. Lastly, IDWT is applied to obtain the watermarked image. As it can be observed from Figs. 4 and 5, the DWT/DMWT is applied separately to each of the red, green, and blue components of an image. It is worth noting that by embedding the watermark into the RGB domain, the effective chip rate of the system is trebled. 3.2 Watermark detection The overall watermark recovery process is shown in Fig. 6. Two scenarios have been considered for recovering the watermark. First, the 1-bit watermark is recovered. The recovered 1-bit watermark shows whether any attack on the watermarked image has taken place or not. The process starts by reading the watermarked image and splitting it into its three colour components R, G, and B. The LH, HL sub-bands are then extracted and the WTMM is computed. This is followed by merging all coefficients in a large contiguous block just like for embedding. The 1-bit watermark is then recovered by cross-correlating the merged coefficients with the same PN sequence that was used for generating the 1-bit watermark. At this stage, if a single large cross-correlation peak is found, this signals that the watermarked image has not suffered any desynchronisation attacks as a result of some geometrical attack and therefore the multi-bit watermark can be safely recovered. Fig. 6Open in figure viewerPowerPoint Proposed watermark recovery process In the second scenario, a single large peak is not found. Rather, many smaller peaks are observed. This indicates that there is something wrong with the watermarked image which has likely been subjected to a geometric attack. In this scenario, the attack needs to be identified and the watermarked image must be first corrected and brought back in sync with the PN sequence, by undoing the geometrical transformation before being able to successfully recover the multi-bit watermark. The multi-bit watermark recovery process is presented in Fig. 7. First, the watermarked image is read. Then, it is split into its R, G, and B components. DWT is applied to each component. Then, the HH sub-band is extracted from each of the components. The coefficients are then merged into one larger block. Finally, cross-correlation is computed between the merged coefficients and the same PN sequence that was generated during the embedding of the multi-bit watermark. Finally, the multi-bit watermark is recovered. It should be noted that in Stage 2, the multi-bit watermark is blindly recovered via cross-correlation from the HH sub-bands of the R, G, and B components of the watermarked image. The only data required for watermark recovery is the watermarked image itself and the secret key used to generate the original PN sequence during embedding. This is one of the key advantages of spread spectrum-based watermarking techniques. Fig. 7Open in figure viewerPowerPoint Stage 2 of the proposed watermark recovery scheme As noted earlier, if during Stage 1 a single large cross-correlation peak cannot be found, then this outcome points to the scenario in which a geometric attack has taken place and the watermark recovery process enters an intermediate stage (Stage 1.5) designed to detect, undo the attack and resynchronise the watermark, before Stage 2 can be applied. In the case of this second scenario, in which the watermarked image has been geometrically attacked, the steps taken to counter the manipulation and to recover the multi-bit watermark logo from the resynchronised watermarked image are shown in Fig. 8. Attack parameters are determined via an extensive search carried out in a given attack parameter range. The attacked watermarked image is subjected to a number of successive geometrical transformations in this range. As shown in Fig. 8, DWT is applied to both images and WTMM is computed. Cross-correlation is performed between the two outputs for each transform parameter in this range. Fig. 8Open in figure viewerPowerPoint Stage 1.5 of the proposed watermark recovery process The transform parameters for which the cross-correlation peak is maximum represent the detected attack parameters. This mechanism can be used to recover common geometrical transforms such as RST. Finally, once identified, the transform parameters are used to correct the attacked watermarked image, by undoing the attack and resynchronising the watermark to allow the successful recovery of the logo at Stage 2. The search algorithm works as follows. Once DWT and WTMM are computed, then cross-correlation of the WTMM of the attacked watermarked image and the WTMM of the rotated version of this image is repeatedly computed step-by-step and the value of each cross-correlation peak is recorded for each rotation step, as part of a loop. Once the loop completes, the rotation value that corresponds to the maximum recorded cross-correlation peak value amongst all other recorded cross-correlation values represents the identified attack parameter itself. Once the attack parameter has been identified in this way, this value is used to undo the attack and correct the geometrically attacked watermarked image. 4 Results and discussion All the experiments were performed using MATLAB R2016. The experiments were performed on a MacBook Pro with a 2.2 GHz Intel Core i7 microprocessor, 16 GB DDR3 RAM, Intel Iris Pro 1536 MB graphics card and OS X El Capitan operating system. Two metrics were used to objectively evaluate the performance of the proposed algorithm. These are the normalised cross-correlation (NCC) and the bit error rate (BER). 4.1 HVS considerations The HVS has been taken into consideration while assigning the embedding weights for the red, green, and blue components of the image. As the human eye is less sensitive to blue, the blue colour can be weighted higher than the red and green components. Many different weight combinations were empirically tested. After experimenting with different weight factor combinations, the following weights were found to provide a good trade-off between robustness and quality of watermarked images: red (0.55), green (0.42), and blue (0.87). This weight assignment is also consistent with the fact that the human eye is more sensitive to changes in the green colour and least sensitive to changes in the blue colour. Hence, the smallest weight has been assigned to the green colour (i.e. lowest embedding strength) while the largest weight has been assigned to the blue colour (i.e. highest embedding strength.) 4.2 Test dataset The test dataset for the experiments included six cover images which are well-known and publicly available. The resolutions of these images are either 256 × 256 or 512 × 512. For each resolution, different images were used covering a broad range of characteristics. Two different black and white (1 bit per pixel) logo images of different sizes were used. These are the 'TEST' logo image which has 50 × 20 pixels and the 'ME' logo image which has 21 × 10 pixels. These logos translate to a watermark length of 1000 bits and 210 bits, respectively. Black and white logo images were chosen to keep the watermark length manageable. The logo images are shown in Fig. 9. Fig. 9Open in figure viewerPowerPoint 'TEST' and 'ME' logo images 4.3 Attack types The main focus of the experiments is on demonstrating the robustness of the proposed algorithm against geometric attacks. These attacks include RST. For testing robustness against rotation, the images are rotated by different angles from 1° to 359° and for each rotated angle the watermark is detected and recovered. Similarly, for testing robustness against translation, the image is shifted using different pixel offset values and in each case the watermark is detected and recovered. Lastly, to test the robustness of the proposed algorithm against scaling, the image is scaled both up and down by different scaling factors and in each case the watermark is detected and recovered. 4.4 Discussion The proposed watermarking scheme can efficiently detect watermarks even after the cover image has undergone geometric attacks of varying intensities. This section demonstrates the watermark detection and recovery capabilities of the proposed watermarking scheme. For this purpose, both the 1-bit and multi-bit (logo) watermarks are first embedded in a cover image. The watermarked image is then subjected to RST and in each case, the proposed scheme is used to find the right transform parameters and undo them. Fig. 10 shows the detection result for the 1-bit watermark. It can be seen from Fig. 10 that since a peak value of 1 has been achieved, with no side peaks, no geometrical attack has been detected, and the 1-bit watermark is successfully extracted, indicating that no attack took place and that it is safe to proceed to Stage 2 and recover the embedded logo. It is worth noting that the profile of NCC values can also indicate that a desynchronisation-type attack took place and therefore the logo watermark image cannot be recovered without first resynchronising the image by undoing the attack. A typical example of this is illustrated in Fig. 11 where many peaks of similar amplitudes can be seen. The presence of many peak values indicates that the watermarked image has been subjected to some geometrical manipulation/attack which would need to be identified and corrected before the actual logo watermark image can be recovered. Fig. 10Open in figure viewerPowerPoint Detection of 1-bit watermark Fig. 11Open in figure viewerPowerPoint In case of an attack, the NCC profile shows many peaks The results for the recovery of the multi-bit watermark are shown in Table 1 for the 'TEST' logo image and in Table 2 for the 'ME' logo image, respectively. The multiwavelet used is Cardbal2. Table 1. Recovering the logo watermark in case of no attack Image PSNR, dB Normalised cross-correlation, NCC Bit error rate, BER Lena 37.24 1 0 pepper 37.29 1 0 Barbara 36.44 1 0 The results are shown for the 'TEST' logo and cover images of size 512 × 512. Table 2. Recovering the logo watermark in case of no attack Image PSNR, dB Normalised cross-correlation, NCC Bit error rate, BER Lena 42.447 1 0 pepper 40.423 1 0 Barbara 42.216 1 0 The results are shown for the 'ME' logo and cover images of size 512 × 512. Fig. 12 shows examples of rotation attacks, detection of attack parameters and the restored images. Stage 1.5 of the proposed algorithm is applied in order to detect the amounts of rotation and to undo the rotation attacks. The position of the maximum value of the cross-correlation peaks indicates the amounts of rotation detected. Fig. 13 shows examples of scaling attacks. Here, again the scaling attack parameters are detected and undone using Stage 1.5 of the proposed algorithm. Finally, Fig. 14 shows examples of translation attacks, detection of translation attack parameters, and the recovered images. Fig. 12Open in figure viewerPowerPoint Examples of rotation attacks, detection of attack parameters, and the restored images (a)–(c) Rotated images (Lena, 15°; Pepper, 130°; Barbara, 100°), (d)–(f) Corresponding detectedparameters, (g)–(i) Corresponding recoveredimages Fig. 13Open in figure viewerPowerPoint Examples of scaling attacks, detection of attack parameters, and the restored images (a)–(c) Scaled images (Lena, 0.5; Pepper, 1.25; Barbara, 0.9), (d)–(f) Corresponding detectedparameters, (g)–(i) Corresponding recoveredimages Fig. 14Open in figure viewerPowerPoint Examples of translation attacks, detection of attack parameters, and the restored images (a)–(c) Translated images (Lena, ( + 80, −80); Pepper, ( + 64,−64); Barbara, ( + 48, −48)), (d)–(f) Corresponding detectedparameters, (g)–(i) Corresponding recoveredimages The results presented in Tables 3–6 demonstrate the capability of the proposed method to detect the amount of rotation, scaling, or translation that the watermarked image is subjected to. The results in Tables 3 and 4 are for the 'TEST' logo watermark and for cover images of resolution 512 × 512 and 256 × 256, respectively. Similarly, the results in Tables 5 and 6 are for the 'ME' logo watermark and for cover images of resolution 512 × 512 and 256 × 256, respectively. For rotation attacks, the watermarked image is rotated by various degrees (such as 40°, 90°, 120°, and 150°) and after undergoing Step 1.5 of the proposed scheme, the distortion parameters are successfully detected with the help of WTMM and cross-correlation. For scaling attacks, the watermarked image is scaled by different scaling factors (such as 0.4, 0.8, 1.25, and 1.4) and the values of these scaling factors are also successfully detected as part of Step 1.5 of the proposed scheme. Similarly, for translation, the watermarked image is subjected to various translation offset values (such as [12, 24], [66, 88], [86, 124] and [176, 176]) which are also successfully detected. Table 3. Distortion parameter detection using the proposed method Attack/Image Lena Pepper Barbara distortion parameter D N