A Reframed Watermark Extraction Approach Using the ABC Algorithm

Chinese Journal of ElectronicsVolume 30, Issue 4 p. 736-742 Original Research PaperFree Access A Reframed Watermark Extraction Approach Using the ABC Algorithm KRICHA Anis, Corresponding Author KRICHA Anis anis.kricha@enim.rnu.tn LATIS Lab, National School of Engineers of Sousse, University of Sousse, Sousse, 4023 Tunisia National School of Engineers of Monastir, University of Monastir, Monastir, 5035 TunisiaSearch for more papers by this authorKRICHA Zied, Corresponding Author KRICHA Zied kricha.zied@gmail.com Higher Institute of Applied Science and Technology of Sousse, University of Sousse, Sousse, 4003 Tunisia ESIR Research Unit, National School of Engineers of Monastir, University of Monastir, Monastir, 5035 TunisiaSearch for more papers by this authorSAKLY Anis, Corresponding Author SAKLY Anis anis_sakly@yahoo.fr National School of Engineers of Monastir, University of Monastir, Monastir, 5035 Tunisia ESIR Research Unit, National School of Engineers of Monastir, University of Monastir, Monastir, 5035 TunisiaSearch for more papers by this author KRICHA Anis, Corresponding Author KRICHA Anis anis.kricha@enim.rnu.tn LATIS Lab, National School of Engineers of Sousse, University of Sousse, Sousse, 4023 Tunisia National School of Engineers of Monastir, University of Monastir, Monastir, 5035 TunisiaSearch for more papers by this authorKRICHA Zied, Corresponding Author KRICHA Zied kricha.zied@gmail.com Higher Institute of Applied Science and Technology of Sousse, University of Sousse, Sousse, 4003 Tunisia ESIR Research Unit, National School of Engineers of Monastir, University of Monastir, Monastir, 5035 TunisiaSearch for more papers by this authorSAKLY Anis, Corresponding Author SAKLY Anis anis_sakly@yahoo.fr National School of Engineers of Monastir, University of Monastir, Monastir, 5035 Tunisia ESIR Research Unit, National School of Engineers of Monastir, University of Monastir, Monastir, 5035 TunisiaSearch for more papers by this author First published: 01 July 2021 https://doi.org/10.1049/cje.2021.05.016Citations: 2AboutSectionsPDF 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 onFacebookTwitterLinkedInRedditWechat Abstract The fast development and the rapid spread of information technology have raised the issue of digital-image copyright protection, which was mostly addressed through robust watermarking. To ensure the survival of the watermark after attacks, most works have been making use of an additional step at the embedding phase, and by so making the scheme unsuitable for real-time implementation. However, to be effective and secure, it is recommended to combine the watermarking algorithm and the capture sensor in one device. In this paper, we brought the extra stage to the detection side. We firstly studied the effect of some signal processing operations on a watermarked image, then we proposed a general model for watermark extraction whose parameters were determined using the artificial bee colony. The efficacy of the proposed method was validated by comparing its robustness with two state-of-art schemes focusing on the watermark extraction. I. Introduction The wide usage of the cyberspace has allowed the multimedia material to be exchanged and freely utilized. Therefore, critical contents might be subject to detrimental modification or contravention of intellectual property rights. Digital watermarking is regarded as a reliable option for safeguarding multimedia material across an unsafe shared channel. Thus, it is often the kernel of numerous solutions including copy control, ownership identification, copyright protection, tamper detection and broadcast monitoring. Watermarking is the technique of inserting a digital data named the watermark into a cover digital material. In this work, the cover material is a greyscale digital image. The performance of a watermarking method is evaluated according to three important characteristics: the quality of the resulting image, the Hiding capacity (HC), and the similarity between the inserted and retrieved watermark after attack, known as robustness. The field of application defines the required balance between these three characteristics. For instance, to check for copyright allegations, the capacity of the scheme may be sacrificed as a short message is sufficient to verify the owner, unlike the robustness and imperceptibility that cannot be compromised. Yet, it stills complicated to realize robustness in conjunction with low distortion because of their discrepancy conduct among one another, not to mention if we add the HC constraint. To resolve this dilemma, some authors have been making use of meta-heuristic optimization techniques to determine the embedding preferences that offer the suitable settlement between these properties[1]. Meta-heuristic algorithms are designed to mimic the social behaviour of animals when they are usually scavenging for a unique objective: food. Hence early works optimize one objective (quality, robustness or HC) and generate the remaining ones according to a specific or variable embedding parameter. For example, the authors of Ref.[2] chose to promote the robustness of their scheme by means of particle swarm optimization. Ref.[3] focused on the quality of the watermarked image by exploiting the Bee Algorithm. In Ref.[4], the capacity of the watermark was the main concern, the authors made use of the genetic algorithm to determine the optimal embedding rate. However, the optimization of one objective solely limits the scheme's performance. To overcome this limitation, the concept of weighting factors was introduced, where each watermark characteristics (imperceptibility, HC, and robustness) are weighted according to the application requirements, then summed to define one objective function[5-10]. For instance, Shen et al.[8] defined the objective function using correlation coefficient to represent both the imperceptibility and the robustness; in the former between the original image and the watermaked one, and in the latter between the inserted watermark (image) and the retrieved one. In Refs.[9, 10], the imperceptibility was assessed using the PSNR metric, and the robustness using normalized cross-correlation. Even so, the literature does not consider the weighted sum technique as a multi-objective optimization but rather a single-objective approach[11]. Bringing the watermarking balance problem to a veritable multi-objective optimization issue is not common due to the challenge of realization. This problem was addressed in Ref.[12], where the authors limited the search space of the artificial bee colony algorithm to solutions complying with a given quality. Regardless of the used algorithm and the fitness function formulation, the previous schemes[2-12] share the intention of optimizing the embedding process. However, such optimization requires additional computational resource, which makes these schemes unsuitable for real-time applications. In this paper, similar to the works presented in Refs.[13-15], we put our attention on the extraction process where real-time requirements are less severe. In Ref.[13], Kang et al. modelled the channel distortion as a combination of a fading factor searched exhaustively, and an additive noise with a non-zero mean, estimated using the Fourier analysis of the probability density function in accordance to Ref.[14]. In Ref.[15], the hidden bits are classified using the Support vector machine (SVM) algorithm, where twelve statistical properties of a reference message were used to train the SVM. The reference message, available at both the embedding and detection stages, is used to carry information about the channel distortion. The novelty of this paper on regards to previous works presented in Refs.[13-15] is the extraction function describing the channel distortion and the use of an optimization algorithm to determine its parameters. The remainder of this paper is organized as follows. Section II elucidates a detailed exposition of the proposed watermarking scheme. Experimental results towards robustness against typical signal processing attacks are given in Section III. Finally, Section IV draws conclusions. II. The Proposed Scheme The objective of the proposed scheme is to adapt the extraction process with any changes in the watermarked image due to signal processing attacks. We used a reference message (known message) to evaluate these changes offline and online. The offline evaluation enabled us to propose a new extraction model, and the online evaluation to adjust that model according to the specification of the attack. The detailed insertion and detection processes are presented hereafter. 1. Embedding scheme The generic embedding scheme of a referenced watermarking algorithm is given in Fig.1. The inserted watermark W is composed of two messages R and P, each serving for a different purpose. Fig. 1Open in figure viewerPowerPoint Watermark embedding process Fig. 2Open in figure viewerPowerPoint Watermark extraction process The message P contains the proprietor's identification used to ascertain any claim of ownership, and so it represents the watermark payload. The message R is generated by means of a Pseudo-random number generator (PRNG) based on a secret key k 1 . The PRNG assures the diversity, the security and the transfer of R to the watermark extractor, where it is utilized to trace back any alteration due to attacks, but provided it is distributed throughout the host. Therefore, the two messages ( P and R) are shuffled and combined to form the watermark W to embed. The energy of the original image is compressed using the Discrete wavelet transform (DWT) with 4 decomposition levels, and the L L 4 sub-band is selected due to its good trade-off between robustness and imperceptibility. W is then embedded in the L L 4 to generate a new watermarked sub-band L L ⌣ 4 using Q I M Δ ( h , w ) . The embedding function Q I M Δ ( h , w ) , given by Eq.(1), inserts a bit w ∈ { 0 , 1 } in a host h using a quantization step Δ. Q I M Δ ( h , w ) = Q ( h + Δ 4 , Δ ) − Δ 4 ) , if w = 1 Q ( h − Δ 4 , Δ ) + Δ 4 ) , otherwise (1) Q ( h , Δ ) = Δ ⌊ h Δ ⌋ + Δ 2 (2) Finally, the inverse DWT (iDWT) is performed to obtain the watermarked image. 2. Reframed watermark extraction (RWE) scheme Prior to the watermark extraction, the watermarked image goes through four levels of DWT (same as in the embedding phase). The L L ⌣ 4 sub-band is then shuffled using the key k 2 to separate the two host-vectors H ⌣ R and H ⌣ P of R and P respectively. In the channel distortion-free case, the minimum Euclidean distance is employed to determine the embedded bit (w) from the carrier ( h ⌣ ) according to Eq.(3). w = arg min x ∈ { 0 , 1 } h ˇ - Q I M Δ ( h ˇ , x ) (3) However, a robust watermarking scheme should achieve more robustness, where even after attacks, the inserted watermark should still be detectable. For that purpose, the R message will be used to enhance the robustness of the watermarking scheme. In fact, R represents known samples of W that will go through the same channel distortion as P. Therefore, if we can adapt Eq.(3) to the attacked form of H ⌣ R , referred by H R ^ , we are adapting Eq.(3) to the attacked form of H ⌣ P , referred by H P ^ , and hence boosting the robustness of the scheme. Unfortunately, the literature lacks of an accurate channel model. In fact, only few works had focused on the modulation of the channel distortion. The basic assumption was to consider it as a constant amplitude attenuation[14]; the authors of Ref.[13] regarded it as a combination of amplitude scaling and Additive white gaussian noise (AWGN). A more general paradigm was described in Ref.[16] as AWGN and Jitter. Even though these different channel models propositions, the exact effect of attacks on a watermarked image is undetermined. Hence, we propose to analyse the attack's impacts on the host H ⌣ R and the extraction of the watermark so we can suggest a more accurate channel distortion model. We used the insertion scheme previously described with R and P lengths equal to 0 and 512 bits respectively; the R size is maximized to cover the largest possible range of H ⌣ R . We put the watermarked image under some signal processing attacks, and each time we reported the values of H R ^ according to the carried bits. The depicted results in Fig.3 show that, when No-attack (NA) is applied to the watermarked image, the detection frames generated by Eq.(2) fit well with the inserted bits. However, if the image goes through a signal processing operation, such as median filter or JPEG compression, these frames can no longer determine the right embedded bits. We notice that the H R ^ values are spread for Median filter (5×5) (MF5), Gamma correction 0.8 (GC), JPEG compression with a quality factor equal to 20 (J20), Speckle noise (SN) and Histogram equalization (HE). For Colour reduction (CR) and Intensity scaling (IS) the host's ranges are contracted. Moreover, the spread and contraction are associated with a phase shifting. Fig. 3Open in figure viewerPowerPoint Effects of different attacks on the extraction of the watermark In Ref.[13], Kang et al. considered the contraction as a constant fading factor reflecting a change of the quantization step, but the non-uniform spread of values was not used to improve the robustness. Hence, we proposed a new watermark extraction where each previously described change (fading, spread and shifting) were taken into account. The spread and fading of frames are defined according to Eq.(4), where α Δ is the new basic frame, δ is the variation between two adjacent frames, and s is the projection of h ^ into a new domain obeying to Eq.(4). Δ ^ t = α Δ + t δ (4) h ^ = ∫ 0 s ( Δ ^ t ) d t = ∫ 0 s ( α Δ + t δ ) d t = α Δ s + δ s 2 2 (5) ⇒ s = h ^ α Δ , if δ = 0 s = - α Δ + ( α Δ ) 2 + 2 α Δ h ^ δ , otherwise (6) The shifting φ is introduced in Eq.(7), where we replace h ^ by | h ^ - φ | . ⇒ s = | h ^ - φ | α Δ , if δ = 0 s = - α Δ + ( α Δ ) 2 + 2 α Δ | h ^ - φ | δ , otherwise (7) The extraction function is given by Eq.(8) which can fit a wider range of channel models than previous works. w ^ = arg min x ∈ { 0 , 1 } s - Q I M 2 ( s , x ) (8) The parameters α, δ and φ define the variation (if exists) of the detection regions, thus they should be carefully determined. As the variation of one parameter affects the effect of the others, they should be determined as a whole. Thus, we made use of the Artificial bee colony (ABC) to determine the parameters of the watermark extractor. The combination of the new channel distortion model and the ABC algorithm, to find its parameters, allows a better estimation of the applied attack and hence a better robustness enhancement is expected. In fact, the scheme in Ref.[13] is a special case of the proposed RWE method. The ABC is considered as a meta-heuristic algorithm capable to achieve satisfactory solutions to non-smooth, non-linear, and ill-conceived problems that standard optimization techniques (e.g. gradient-based) are incapable to resolve. ABC algorithm emulates the intelligent conduct of honeybees foraging for nourishment. Honey bees in a beehive are split into sets called swarm, each swarm achieve its task through social cooperation. Similarly to the beehive, ABC algorithm employs three types of bees: employed, onlooker and scout bees. Employs bees are responsible for exploring food sources and share related information with onlooker bees so they can start the exploration of these sources. Once a food source is depleted, onlooker bees in charge are replaced by scout bees. The computational complexity of ABC is given by O ( D m 2 i ) where D is the problem's dimension, N the number of bees, and i the number of iterations. This extra complexity will affect the extraction scheme, but that of the embedding will remain unchanged. The ABC algorithm is used to determine the optimal channel distortion parameters ( α, Δ, and δ) by finding the best match between the embedded reference message R and the extracted one from H R ^ using Eq.(8). These parameters will be used to extract P from H P ^ . The extraction process of P from H P ^ using the ABC algorithm is described by the following steps: Step 1: At first, random food sources are created. Each source represents a candidate solution for the optimization problem, and hence it is represented by the triplet α, Δ, and δ. Step 2: Employed bees lead the exploitation phase by looking for new sources around the available food sources. Each new source is evaluated according to the objective function given by Eq.(9) O b j e c t i v e = 1 B E R ( R , R ^ ) (9) where R ^ is the extracted reference message from H R ^ using Eq.(8). Step 3: Onlooker bees evaluate the sources received from all employed bees and generate modified channel distortion parameters based on the probability values. Step 4: If the results of an employed bee cannot be improved after a number of iterations, the exhausted source is abandoned and replaced by new random source generated by a scout bee. Step 5: Iteratively repeat steps two to four until requirement is satisfied: BER = 0 or maximum number of iterations is reached. Step 6: Extract the payload watermark from H P ^ using the optimal found parameters. III. Performance Evaluation We conducted the investigational tests on six frequently employed 8-bit greyscale images including Lena, Peppers, Baboon, Airport, Elaine, and House. We compared the performance of the proposed extraction scheme against two state-of-art works that benefited from a reference message[9,11] to improve the robustness. To evaluate the loss of quality caused by the watermark embedding, we used the Peak signal-to-noise ratio (PSNR). We assessed the robustness of our watermarking algorithm by the Bit error rate (BER) among the inserted watermark P = { p n } and the extracted watermark P ^ = { p ^ n } . BER ( P , P ^ ) = ∑ n = 1 N p p n ⊕ p ^ n N p (10) where N p denotes the size of P. For a fair comparison, we evaluated the scheme's robustness for the same HC and quality. Towards that end, we used different sub-bands and different sizes of reference and payload messages. To compare our work with Ref.[13], we selected the same sub-band L L 4 , as well as the same embedding capacity of 156 and 868 bits for R and P respectively. However, Ref.[15] employed a bloc insertion approach in the L H 3 sub-band. Hence, in order to maintain a similar embedding strength, we used four levels of DWT. For the quality's rapprochement, we selected the H H 4 sub-band as it offers the best robustness in comparison with other sub-bands due to the limited numbers of detection frames, which reduce the propagation of error between detection frames. Given that the H H 4 adversely affects the imperceptibility, we decided to use a higher PSNR that provides similar quality to Ref.[15]. For both schemes, the distortion of the watermarked image due to the watermark embedding is judged acceptable (subjective test). We targeted different PSNR values according to the image's texture and smoothness (Table 1). Table 1. PSNR(dB) of the watermarked images with respect to the original ones Image\Method Ref.[13] Proposed Ref.[15] Proposed Lena 43.5 43.56 41.49 42.38 Peppers 42.6 42.53 38.20 42.73 Baboon 41.1 41.64 39.19 39.28 Boat 42.5 42.61 36.52 43.17 Man 43.23 43.23 40.59 42.37 Airport 43.06 43.06 40.13 42.22 Elaine 43.12 43.12 38.8 42.27 House 43.3 43.3 39.39 42.52 Livingroom 43.1 43.1 38.36 42.38 To prove the validity of the proposed method, we show in Fig.4 the constructed frames after histogram equalization attack; we embedded a reference message with a size of 512 bits alongside with a payload message with the same size. Fig. 4Open in figure viewerPowerPoint Generated extraction frames using ABC algorithm after histogram equalization attack The watermark is inserted in the H H 4 sub-band of the Baboon image. We notice that the detection regions are well described and accurately estimated using the ABC optimization algorithm. Where from 512 bits constituting the payload message, only 11 are wrongly determined. To evaluate the robustness of the proposed RWE scheme, we considered the following signal processing attacks: Median Filter with (3×3) and (5×5) masks: MF3 and MF5 respectively. Average Filter with (3×3) and (5×5) masks: AF3 and AF5 respectively. JPEG compression with quality factor of 30 (J30) and 20 (J20). Colour reduction from 8 bits to 4 bits: CR. Intensity scaling from 256 to 32 levels: IS. Gamma correction with a gamma values between {1.2, 0.8}: G1.2 and G0.8 respectively. Histogram equalization: HE. Speckle noise with variance 0.01: SN. Salt and Pepper noise with variance 0.01: SP. Table 2 and Table 3 outline the obtained results in terms of BER and the corresponding robustness in Ref.[13] and Ref.[15], respectively. The best results are marked in bold and the non-available with the "-" symbol. Note that the results of Ref.[13] and Ref.[15] mentioned in Tables 1-3 were directly excerpted from the original papers. Table 2. Comparison of the BER of the RWE proposed method with the method of Ref.[13] Attack Lena Boat Peppers Baboon Man Airport Elaine House Livingroom RWE Ref.[13] RWE Ref.[13] RWE Ref.[13] RWE Ref.[13] RWE Ref.[13] RWE Ref.[13] RWE Ref.[13] RWE Ref.[13] RWE Ref.[13] MF3 0.017 0.09 0.045 0.21 0.014 0.17 0.127 0.13 0.008 0.041 0.003 0.02 0.002 0.14 0 0.02 0.013 0.046 MF5 0.122 0.16 0.206 0.24 0.061 0.17 0.265 0.28 0.109 0.353 0.02 0.226 0.011 0.105 0.01 0.024 0.079 0.111 AF5 0.055 – 0.049 – 0.055 – 0.034 – 0.01 – 0.002 – 0.004 – 0.006 – 0.004 – AF7 0.066 – 0.088 – 0.064 – 0.064 – 0.07 – 0.14 – 0.024 – 0.012 – 0.026 – J40 0.002 – 0.002 – 0.002 – 0.002 – 0 – 0 – 0 – 0 – 0 – J30 0.005 – 0.006 – 0.009 – 0.005 – 0 – 0.02 – 0 – 0 – 0 – J20 0.041 – 0.027 – 0.03 – 0.016 – 0.012 – 0.013 – 0.004 – 0.057 – 0.013 – CR 0.152 0.24 0.052 0.23 0.088 0.08 0.003 0.01 0.01 0.421 0.046 0.536 0.009 0.163 0.118 0.502 0.002 0.235 IS 0.002 0.03 0 0 0 0 0 0 0 0.407 0.036 0.248 0 0.256 0 0.149 0 0.24 G1.2 0.041 0.21 0.048 0.16 0.018 0.16 0.006 0.09 0.033 0.119 0.023 0.226 0 0.024 0.004 0.024 0.003 0.023 G0.8 0.046 0.22 0.05 0.14 0.178 0.18 0.008 0.06 0.049 0.086 0.011 0.028 0 0.026 0.009 0.066 0.003 0.017 HE 0.308 – 0.376 – 0.176 – 0.241 – 0.089 – 0.105 – 0.094 – 0.284 – 0.134 – SN 0.088 – 0.076 – 0.044 – 0.047 – 0.036 – 0.01 – 0.066 – 0.095 – 0.062 – SP 0.079 – 0.061 – 0.063 – 0.039 – 0.089 – 0.024 – 0.071 – 0.076 – 0.063 – Table 3. Comparison of the BER of the proposed RWE method with the method of Ref.[15] Attack Lena Boat Peppers Baboon Man Airport Elaine House Livingroom RWE Ref.[15] RWE Ref.[15] RWE Ref.[15] RWE Ref.[15] RWE Ref.[15] RWE Ref.[15] RWE Ref.[15] RWE Ref.[15] RWE Ref.[15] MF3 0.004 0.037 0.02 0.037 0.006 0.041 0 0.05 0.005 0.042 0.001 0.058 0.001 0.033 0.003 0.064 0.009 0.037 MF5 0.033 0.089 0.069 0.105 0.033 0.111 0.08 0.093 0.035 0.085 0.025 0.115 0.005 0.089 0.007 0.119 0.052 0.08 AF5 0.005 0.068 0.006 0.072 0.005 0.019 0 0.039 0.003 0.054 0.001 0.054 0 0.062 0.003 0.095 0.001 0.052 AF7 0.029 0.109 0.038 0.103 0.022 0.107 0.015 0.072 0.058 0.091 0.011 0.093 0.017 0.093 0.017 0.126 0.017 0.082 J40 0 0.015 0 0.009 0 0.015 0 0.019 0 0.011 0 0.013 0 0.011 0 0.023 0 0.015 J30 0 0.019 0 0.017 0 0.027 0 0.023 0 0.025 0 0.029 0 0.025 0 0.031 0 0.025 J20 0.009 0.046 0.012 0.044 0.009 0.031 0 0.035 0.007 0.044 0 0.037 0.001 0.035 0.031 0.044 0.007 0.035 CR 0.011 – 0.004 – 0.009 – 0 – 0.033 – 0.048 – 0.009 – 0.062 – 0.023 – IS 0 – 0 – 0 – 0 – 0 – 0 – 0 – 0.023 – 0 – G1.2 0 – 0.001 – 0.02 – 0 – 0.031 – 0 – 0 – 0.003 – 0.001 – G0.8 0.001 – 0.002 – 0.032 – 0 – 0.039 – 0 – 0 – 0.007 – 0 – HE 0.054 0.033 0.233 0.07 0.113 0.031 0.023 0.052 0.085 0.035 0.265 0.06 0.089 0.05 0.285 0.089 0.105 0.08 SN 0.041 0.044 0.065 0.06 0.036 0.054 0.004 0.042 0.019 0.039 0.005 0.037 0.06 0.06 0.058 0.06 0.039 0.031 SP 0.049 0.042 0.064 0.044 0.066 0.04 0.008 0.035 0.052 0.052 0.046 0.048 0.037 0.042 0.048 0.031 0.056 0.017 From Table 2 and Table 3, it is noticed that the RWE scheme assures satisfying results in terms of robustness. The RWE outperforms the detection technique used in Ref.[13] for all attacks, where the mean of the BER is 0.016 against 0.154 in Ref.[13] which represents an improvement of 89%. When compared with Ref.[15], the RWE scheme presents better results in 7 out of 9 attacks, with a BER mean value equal to 0.023 compared to 0.051 obtained in Ref.[15], which represents an improvement of 53%. IV. Conclusions In this paper, we introduced a robust watermarking scheme based on QIM for ownership verification. Generally, such application requires real-time embedding, so no unprotected media exists. The robustness of the proposed scheme relies on the adaptive extraction process of the watermark without affecting the embedding computational complexity. The analysis of some signal processing attacks allowed us to propose a new channel distortion model, where the default detection frames are faded, phased and varied. These parameters were estimated using ABC algorithm along with a reference message used as samples of the new channel model. Experimental results showed significant improvement of robustness compared to similar works. Biographies KRICHA Anis received the engineering degree in electrical engineering, the DEA degree in automatic and signal processing as well as the Ph.D. degree in electrical engineering from the National Engineering Schoolof Tunis (ENIT) in 2001 2004, and 2012 respectively. From 2006 to 2012, he was working as an assistant professor in the Department of Electrical Engineering in the National Engineering School of Monastir (ENIM), University of Monastir, Tunisia. Since 2013 he has been an associate professor in the same school in signal and image processing. He is a member of the Laboratory of Advanced Technology and Intelligent Systems (LATIS). His research interests include mainly image processing, watermarking, computer vision, segmentation, compression, pattern recognition, artificial intelligence, document analysis and recognition. KRICHA Zied (corresponding author) received the Ph.D. degree in electrical engineering from the National School of Engineers in Sousse (Tunisia). He is currently an assistant professor at the Higher Institute of Applied Science and Technology of Sousse. His research interests include software/hardware implementation of digital watermarking systems for image and video authentication. SAKLY Anis received the electrical engineering diploma in 1994 from the National Engineering School of Monastir (ENIM), then the Ph.D. degree in electrical engineering in 2005 from the National Engineering School of Tunis (ENIT). He is currently a Professor at ENIM. His research interests are in analysis and synthesis of intelligent control systems, particularly soft computing-based control approaches. 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