Optical encryption has captured growing attentions in the past two decades owning to its inherent advantages such as parallel signal processing and high dimensional operation^{1, 2}. When irradiating a two-dimensional image vertically with parallel light, all points on the image will be modulated by light simultaneously. Besides, inherent parameters of the optical system (e.g., the amplitude, phase, wavelength, polarization and diffraction distance) can be designed as the security keys of an optical cryptosystem for multi-dimensional encryption². Most of optical encryption techniques are based on the technique proposed by P. Refregier and B. Javidi, and are known as double random phase encryption (DRPE)³, which uses two statistically independent random phase masks (RPMs) as the security keys to scramble an original plaintext image into a stationary white noise. Following this pioneering work, a variety of modifications of DRPE have been developed in other linear canonical transform domains such as the fractional Fourier domain^{4, 5}, Fresnel domain⁶ and wavelet domain⁷. In the meantime, random phase encoding has been incorporated with typical optical signal processing or imaging architectures, such as digital holography^{8, 9}, joint transform correlator¹⁰, interference¹¹, diffractive imaging¹², computational ghost imaging¹³ and ptychography¹⁴. Note that the security performance of a cryptosystem is one of the major concerns. A cryptosystem can be considered sufficiently secure if and only if it can endure safety assessment by cryptanalysis. Cryptanalysis refers to the study on cryptosystems that aims to identify any defect in them that will permit retrieval of the plaintext from the ciphertext, without necessarily knowing the secret key¹⁵. On the other hand, a variety of attacks on existing optical cryptosystems can facilitate the development of security-enhanced optical cryptosystems^16-18. As fueled by the advancement of optical cryptography, many optical cryptanalysis methods have been proposed as well, ranging from the chosen plaintext attacks (CPA)^{19, 20}, known plaintext attacks (KPA)^{21, 22}, and ciphertext-only attacks (COA)^23-25. Among these optical cryptanalysis methods, CPA and KPA require attackers to access more resources and more control of the encryption system, and COA is usually considered as the most critical yet challenging problem since only a minimum resource is available to break the cryptosystem. In the existing optical cryptanalysis methods, the COA problem is usually transferred to a phase retrieval problem with single intensity measurement. The iterative phase retrieval algorithm^26-34 has been employed to solve this problem by exploiting an estimated signal domain support²⁶ and a given frequency domain constraint. However, it is time-consuming since it usually needs thousands of iterations to converge to a feasible solution. It has also been demonstrated that speckle correlation techniques are feasible for COA due to the high similarity between the autocorrelations of the ciphertext and the plaintext³⁵. However, for the coherent DRPE system, the autocorrelation of ciphertext or the energy spectral density is usually contaminated by speckle noise produced by the RPM at the input plane, which is actually shown in a way similar to the “shower-curtain effect”³⁶. One of the methods to remove this kind of speckle noise is spatial averaging, which is done by dividing a ciphertext image into a sequence of sub-images³⁷. But there is a trade-off between the number and the size of sub-images, which limits the spatial resolution of the recovered image.

Over the past few years, deep learning has attracted increasing attentions and found to be highly flexible in solving various types of ill-posed inverse problems in optical sensing and imaging^38-53 such as optical tomography⁴¹, computational ghost imaging^{42, 43}, visual tracking⁴⁴, digital holography^{45, 46}, lensless phase imaging^47-49, as well as imaging through scattering media^50-53. For optical cryptanalysis, deep learning has demonstrated its capability of attacking on several optical cryptosystems such as DRPE and triple random phase encoding⁵⁴, interference/diffraction encryption^55-57 and computer-generated hologram encryption⁵⁸. However, all of the aforementioned learning-based attack methods belong to the category of CPA, which requires a large set of plaintext-ciphertext pairs of a cryptosystem under analysis. This will be challenging because it is unlikely for an attacker to have access to the cryptosystem for such a long time.

Here, we demonstrate methodologically, numerically and experimentally for the first time, to our knowledge, that the use of deep learning can solve the inverse problems in COA against the classical DRPE. To be specific, we develop a two-step deep learning framework that retrieves the plaintext from an intercepted unknown ciphertext alone. For acquiring the training data, we construct a virtual DRPE system that includes different random phase keys to provide the statistically ergodic property of the speckle pattern. We note that the autocorrelation of the ciphertext in DRPE contains the information of the autocorrelation of the plaintext, only that the former one is with some additive speckle noise. Inspired by the principle of speckle correlations, we divide the inverse problem in COA into two inverse problems: one is the removal of the speckle noise from the autocorrelation of the ciphertext, and the other is the retrieval of the plaintext from the noise-free autocorrelation. Accordingly, two cascaded deep neural networks (DNNs) are employed to respectively solve the two specific inverse problems. With appropriate training, the two trained DNNs can be easily used to predict the plaintext image from the unknown ciphertext without knowing the phase keys.

In DRPE, a plaintext can be encrypted into a white noise-like distribution by employing an optical 4f system, where two RPMs serving as the keys are placed at the input plane and Fourier plane, respectively. The optical structure of DRPE is shown in Fig. 1(a). The encryption process can be mathematically expressed as

$C(x,y) = {{\rm{FT}} ^{ - 1}}\{ {\rm{FT}} \{ P(x,y) \cdot M(x,y)\} \cdot N(u,v)\}\;, $ (1)

where ${\rm{FT}} \{ \cdot \} $ and ${{\rm{FT}} ^{ - 1}}\{ \cdot \} $ represent the Fourier transform and inverse Fourier transform, respectively; $(x,y)$ and $(u,v)$ represent the coordinates of the spatial domain and Fourier domain, $P(x,y)$ and $C(x,y)$ denote the plaintext and the ciphertext respectively; $M(x,y) = $$ \rm{exp} [\rm{i}2{\rm{π}}{r_1}(x,y)]$ and $N(u,v) = \rm{exp} [\rm{i}2{\rm{π}} {r_2}(u,v)]$ represent the two RPMs, where ${r_1}(x,y)$ and ${r_2}(u,v)$ are the statistically independent uniform distribution in the range of $(0,1]$. The decryption procedure of DRPE is the exact inverse process of the encryption with the conjugates of phase keys.

The encryption process of the DRPE can be considered as the forward propagation process (see Fig .1(a)), and it is defined as $C = {{T}}\{ P\}$, where ${{T}}\{ \cdot \} $ denotes the forward model. Optical cryptanalysis is a typical inverse problem that is denoted as $P = {{{T}}^{ - 1}}\{ C\} $ (see Fig .1(b)), aiming to retrieve the plaintext from the corresponding ciphertext. One possible pure data-driven solution is to train a DNN with a set of plaintext-ciphertext pairs. Assuming that a training set of ground-truth plaintexts and their ciphertexts $\{ {P_i},{C_i}\} $ is known. The optical cryptanalysis problem implicitly solved by DNN can be formulated as the following equation

${R_{{\rm{DNN}}}} = \mathop {{\rm{arg}}\; {\rm{min}} }\limits_{\theta \in \Theta } {\left\| {{R_\theta }\{ {C_i}\} - {P_i}} \right\|^2}\;,$ (2)

where ${R_\theta }$ is the mapping function of the DNN, and $\theta $ denotes the parameters of weights and biases. This kind of “end-to-end” mapping method is simple but needs a large number of plaintext-ciphertext pairs in the same optical encryption system, which means that an attacker has the ability to access the cryptosystem in advance.

Nevertheless, for COA, according to Kerchhoff’s principle¹⁵, the attacker is assumed to have access only to a ciphertext and use it alone to retrieve the corresponding plaintext. In practice, for the COA on DRPE, the attacker still has the knowledge of the cryptosystem (e.g., coherent illumination, 4f architecture) except for the random phase keys. Therefore, it is possible to gather a set of training data from a virtual DRPE system that includes a set of randomly generated RPMs placed at the spatial and frequency domain. Since the ciphertext is obtained from the different plaintext and different RPMs, the “end-to-end” mapping DNN carries the burden of learning all of the physical laws.

We have noted that the DRPE cryptosystem is essentially a coherent imaging system and the encryption formulation (Eq. (1)) can be rewritten as

$C(x,y) = P'(x,y) * h(x,y)\;,$ (3)

where the symbol “ $* $” denotes a convolution operation, $P'(x,y) = P(x,y) \cdot M(x,y)$ is the complex field at the input plane, $h(x,y) = {\rm{FT}} \{ N(u,v)\} $ is the point spread function (PSF) of the DRPE system. Then the autocorrelation of ciphertext $Ac(x,y)$ can be written as

$ $$\begin{array}{rl}Ac(x,y)=& C(x,y)\otimes C(x,y)\\ =& [P^{\prime }(x,y)\otimes P^{\prime }(x,y)]\ast [h(x,y)\otimes h(x,y)]\\ =& [P^{\prime }(x,y)\otimes P^{\prime }(x,y)]\ast \delta (x,y),\end{array}$$ $ (4)

where the symbol “ $ \otimes $” denotes the autocorrelation operation, $\delta (x,y)$ is a peaked function. It suggests that the contribution of RPM2 at the Fourier plane can be removed by performing an autocorrelation operation. Therefore, the calculated autocorrelation of the ciphertext can be simplified as

$Ac(x,y) \approx [P'(x,y) \otimes P'(x,y)] = Ap(x,y) + S(x,y)\;,$ (5)

where $Ap(x,y)$ denotes the autocorrelation of plaintext, $S(x,y)$ is the speckle noise term, which refers to the contribution of RPM1. If this speckle noise can be removed as well, the plaintext can be further retrieved. Decorrelation or reconstruction of an object from the modulus of its Fourier transform is a long-standing challenge and it is essentially an ill-posed inverse problem due to the absence of its Fourier phase. Usually, this type of inverse problem can be solved by an iterative phase retrieval algorithm with some prior knowledges such as non-negative and real-valued object and the support area in the object domain^{26, 27}. However, iterative methods are time-consuming, which makes real-time reconstruction a challenge. Recently, deep learning offers an alternative approach to perform such de-correlation tasks^{48, 51, 52}, and U-net shows powerful performance in solving image pixel regression problems⁵⁹. Accordingly, a U-net neural network can be employed to perform the de-correlation task by training the mapping relationship between the autocorrelation pattern (which corresponds to the magnitudes of an object’s Fourier transform) and the real-space object from a large prepared training set. After the training, the de-correlation DNN model can invert an autocorrelation pattern to the corresponding object image in real-time.

Therefore, the problem to be addressed in COA on DRPE can be reformulated as two inverse problems: one is the removal of the speckle noise from the autocorrelation of the ciphertext while the other is the retrieval of the plaintext from the noise-free autocorrelation. Inspired by the aforementioned analysis while aiming at achieving a better performance with limited training data, we propose a two-step deep learning strategy for solving the problems of the COA on DRPE (see Fig. 1(c)). Specifically, the autocorrelation functions of the ciphertext and the plaintext should be calculated first as the feature to be trained. Then two cascaded DNNs are built to solve the two corresponding inverse problems, DNN1 takes the autocorrelation of the ciphertext $Ac(x,y)$ as its input and estimates the plaintext autocorrelation $Ap(x,y)$; DNN2 is trained to predict the final plaintext image $P(x,y)$ from $Ap(x,y)$. Mathematically, these two inverse problems solved by DNN1 and DNN2 can be respectively formulated as

${R_{{\rm{DNN1}}}} = \mathop {{\rm{arg}}\; {\rm{min}} }\limits_{\theta 1 \in \Theta } {\left\| {{R_{\theta 1}}({Ac_{i}}) - {Ap_{i}}} \right\|^2}\;,$ (6)

${R_{{\rm{DNN2}}}} = \mathop {{\rm{arg}}\; {\rm{min}} }\limits_{\theta 2 \in \Theta } {\left\| {{R_{\theta 2}}({Ap_{i}}) - {P_i}} \right\|^2}\;,$ (7)

where ${R_{\rm{DNN1}}}$ and ${R_{\rm{DNN2}}}$ are the mapping functions of DNN1 and DNN2, $\theta 1$ and $\theta 2$ denote the weights and biases of DNN1 and DNN2, respectively. Once the learning step is completed, ${R_{\rm{DNN1}}}$ and ${R_{\rm{DNN2}}}$ can then be used to decipher the plaintext from an unknown ciphertext.

For training the DNNs mentioned above, the training data should be prepared. The objective of DNN1 is to remove the speckle noise from the autocorrelation functions of ciphertexts. In the COA scenario, the intercepted ciphertext might be encrypted with any unknown RPMs. To get the better de-noising performance, the de-noising model should sufficiently encompass the statistical variations across as many RPMs as possible. Usually, different realizations of the speckle patterns can be obtained by coherently illuminating the plaintext with different random phases. This requires the use of many different random phase keys to encrypt the plaintext images to achieve the statistical ergodic property of the speckle pattern. Therefore, a virtual DRPE system (not the real one) should be designed to gather the training data. As illustrated in Fig. 2, a set of randomly generated RPMs are placed at the spatial and frequency domains in this virtual DRPE system to encrypt the plaintext images and obtain the corresponding ciphertext images, which can be expressed as

$$$\begin{array}{rl}{C}_i(x,y)& ={\mathrm{F}\mathrm{T}}^{-1}\{\mathrm{F}\mathrm{T}\{P_i(x,y)\\ & \cdot M_i(x,y)\}\cdot N_i(u,v)\},i=1,2,...,n\end{array}$$\;.$ (8)

Subsequently, the autocorrelations of ciphertexts ${Ac_{i}}(x,y)$ can be calculated by taking an inverse Fourier transform of its power spectrum in accordance with the Wiener–Khinchin theorem, that is

${Ac_{i}}(x,y) = \left| {{{{\rm{FT}} }^{ - 1}}\{ {{\left| {{\rm{FT}} \{ {C_i}(x,y)\} } \right|}^2}\} } \right|, i = 1,2,...,n\;.$ (9)

Meanwhile, the autocorrelations of plaintexts ${Ap_{i}}(x,y)$ can also be calculated from the ground-truth plaintext images, that is

${Ap_{i}}(x,y) = \left| {{{{\rm{FT}} }^{ - 1}}\{ {{\left| {{\rm{FT}} \{ {P_i}(x,y)\} } \right|}^2}\} } \right|, \;\; i = 1,2,...,n\;.$ (10)

In this way, the dataset of the autocorrelations of ciphertexts ${Ac_{i}}(x,y)$ and the autocorrelations of plaintexts ${Ap_{i}}(x,y)$ are set as the inputs and outputs of DNN1 respectively; the dataset of the autocorrelations of plaintexts ${Ap_{i}}(x,y)$ and the ground-truth plaintext images ${P_i}(x,y)$ are set as the inputs and outputs of DNN2, respectively.

To perform the task of de-noising, the popular DnCNN model⁶⁰ is employed as DNN1 in the proposed COA method. The architecture is illustrated in Fig. 3(a). The ciphertext’s autocorrelation image first passes through a standard convolutional layer with filter size 3×3, followed by a rectified linear unit (ReLU), and then passes through ten “BN+Conv+ReLU” blocks successively, which consists of an operation of batch normalization (BN), a standard convolutional layer with the size 3×3 and a ReLU. Finally, a standard convolutional layer with filter size 1×1 is employed to output the noise-free autocorrelation image. We have compared DnCNN model with the extensively-utilized U-net one⁵⁹ on the task of de-noising (See Fig. S1). Both networks have good performance on the removal of speckle noise, but the training time of DnCNN is much shorter than that of the U-net.

However, for the task of de-correlation, a modified U-net architecture⁵⁹ is employed (see Fig. 3(b)), where the encoder path (left side) to extract the feature maps from the input patterns, and the decoder path (right side) to perform pixel-wise regression. The input to the network is the de-noised autocorrelation images with the size 64 pixels ×64 pixels. They first pass through a standard convolutional layer with the filter size 3×3, followed by a 2×2 max pooling operation with the stride 2 for downsampling, and then is successively decimated by four “convolution + dense + downsampling” blocks, where each block consists of a standard convolutional layer with the size 3×3, a dense block, and a 2×2 max pooling layer with the stride 2. A dense block consists of n convolutional layers, where the n^th layer receives the feature-maps of all preceding layers, ${x_0},{x_1},...,{x_{n - 1}}$, as inputs:

${x_n} = {H_n}([{x_0},{x_1},...,{x_{n - 1}}])\;,$ (11)

where the bracket [·] denotes the concatenation of the feature-maps extracted from layers $0,1,...,n - 1$, and H_n(·) refers to a composite function of three consecutive operations: BN, followed by a ReLU and a standard convolution layer with the size 3×3. After passing through the encoder path, the feature maps of the plaintext then successively pass through 3 “upsampling + dense” blocks, where each block consists of an up-convolutional layer with the size 2×2 and a dense block. Finally, another up convolutional layer is employed to perform pixel-wise regression. In addition, skip connections are also employed to pass high-frequency information learned from previous layers down the network toward the output reconstruction. We have also compared two models above on the task of de-correlation, the results are shown in Fig. S2. It is obvious that U-net has better performance than DnCNN for de-correlation.

Numerical simulations have been carried out to demonstrate the validity of the proposed learning-based COA approach. In the following numerical simulations, the size of all the images is set as 64 pixels ×64 pixels. For the training process, a total of 10000 images (5000 digits and 5000 letters) from the MNITS handwritten digit dataset⁶¹ and NIST handwritten letter⁶² were adopted as the dataset of plaintexts. They were resized from 28 pixels ×28 pixels to 32 pixels ×32 pixels and zero-padded 64 pixels ×64 pixels. Simultaneously, 20000 RPMs randomly distributed in the range of (0,2π] with 256 gray-levels were generated by setting the different random seeds (1−10000 for RPM1, 10001−20000 for RPM2), which were used as the phase keys at the spatial and frequency domains of the DRPE system respectively. Subsequently, 10000 corresponding ciphertexts were obtained by encrypting the 10000 plaintext images with this virtual DRPE system. The autocorrelations of ciphertexts and plaintexts were calculated as the input and output of the DNN1, and the autocorrelations of plaintexts and plaintexts itself acted as the input and output of the DNN2. For both DNNs, the loss functions have been defined by the mean absolute error (MAE):

$MAE(A,B) = \frac{1}{N}\sum\limits_{i,j} {[A(i,j) - B(i,j)]} \;,$ (12)

where $A(i,j)$ and $B(i,j)$ denote the output of the DNNs and the ground truth, respectively, N is the number of the pixels. Alternatively, a loss function that is based on mean square error (MSE) can also be used to obtain similar results. We used the Adam optimizer with a learning rate of 0.0005 to optimize the weights and biases of the neural networks. The program was implemented with Python 3.6 on the platform of TensorFlow. A graphics processing unit (NVIDIA GeForce GTX 1050 Ti) was used to expedite the computation. After 10 epochs and 20 epochs, the loss function MAEs of DNN1 and DNN2 become 0.0037 and 0.048, respectively, which implies that the DNNs have been well trained to achieve a good performance for the training dataset.

With the two trained DNNs at hand, now we can perform the COA test. The numerical simulation results are shown in Fig. 4, where the three columns on the left indicate the digits while another three columns on the right show letters. Figure 4(a) shows the given ciphertexts, which are generated with the testing plaintext images (different from the training dataset) in a testing DRPE system (RPMs generated by setting the random seeds as 20001−21000 for RPM1 and 21001−22000 for RPM2). With the given ciphertexts, we can calculate their autocorrelation functions, which are presented inFig. 4(b). We have removed the peaked function $\delta (x,y)$ from the autocorrelation images for displaying. It can be clearly seen that the autocorrelation functions present inerratic edge profiles and is corrupted by random speckle noise. Subsequently, these pre-processed autocorrelation functions are fed into the trained DNN1, and the outputs are presented in Fig. 4(c). Then the outputs from DNN1 are further fed into the trained DNN2 model, the outputs of DNN2 are presented in Fig. 4(d). The ground-truth plaintext images are shown in Fig. 4(e) for comparison. The predicted plaintext images can be visualized and are clearly recognizable despite of the fact that their resolutions are slightly degraded which may be resulted from the convolution operation in the DNN training process.

It should be pointed out that zero-padding of images was applied before the encryption to introduce frequency redundancy. Zero-padding operation actually has been extensively exploited and discussed in signal processing literature^28-33. According to Nyquist-Shannon theorem, a two-dimensional signal can be uniquely specified by the magnitude of its twice oversampled discrete Fourier transformat³⁴. Conventional phase retrieval methods do work well unless the condition of twice oversampling is satisfied. To further validate the proposed method, we try to reconstruct plaintext image from its autocorrelation function without zero-padding operation, which is usually impossible for the conventional iterative phase retrieval algorithm. During the training process, we use the autocorrelation function without zero-padding as the input of the de-correlation DNN model and the test results are presented in Fig. S4. Surprisingly, the images can be recovered with high fidelity from the incomplete autocorrelation pattern, which indicates the de-correlation DNN model still effective even without zero-padding operation.

To quantitatively analyze the reliability of the proposed COA method, we introduce the correlation coefficient (CC) to quantitatively evaluate the quality of the retrieved plaintext images. The CC between image A and image B are defined as follows

$CC(A,B) = \frac{{\sum\nolimits_{i,j} {[A(i,j) - \overline A ][B(i,j) - \overline B ]} }}{{\sqrt {\sum\nolimits_{i,j} {{{[A(i,j) - \overline A ]}^2} \times \sum\nolimits_{i,j} {{{[B(i,j) - \overline B ]}^2}} } } }}\;,$ (13)

where $\bar A$ and $\bar B$ denote average value of $A(i,j)$ and $B(i,j)$. We have calculated the values of the CC of 100 reconstructed plaintext images which are randomly selected out of the total 1000 in the test set, and the results are plotted in Fig. 5(a). The blue circle and orange triangle markers represent the values of the CC related to digits and letters test data, respectively. As expected, most of the CC values are larger than 0.5 and the averaged CC values is 0.816. However, it is also worth noting that there are still a few cases in which the CC values are smaller than 0.5 due to the fact that the retrieved plaintext images rotated 180 degrees, especially for the digits “6” and “9”. The reason for that is the autocorrelations of the prediction and the ground-truth are almost the same, as shown in Fig. 5(b). The results consist of the ambiguity regarding the phase-retrieval inverse problem since the recovery of a signal from its Fourier magnitude alone, in general, does not yield a unique solution³⁴.

Moreover, we have also investigated the robustness of the proposed method against the cropping and the noise. The results are illustrated in Fig. 6. The two images on the left side of Fig. 6(a) respectively present the cropped ciphertexts with cropping ratio 1/16 and 1/4; the two images in the middle of Fig. 6(a) present the ciphertexts added zero-mean Gaussian noise with 0.01 and 0.02 variance; the two images on the right side of Fig. 6(a) present the ciphertexts added salt & pepper noise with 0.01 and 0.02 distribution density. The calculated autocorrelation functions of the corrupted ciphertexts are displayed in Fig. 6(b). The reconstructed plaintext images by the proposed two-step deep-learning-based COA method are shown in Fig. 6(c). For comparison, the reconstructed images by the one-step “end-to-end” method (from the autocorrelation of ciphertext to the plaintext directly) are shown in Fig. 6(d). Obviously, the images shown in Fig. 6(c) can be visualized and recognized while the images shown in Fig. 6(d) are completely different from the ground-truth. To quantitatively evaluate the robust capability, we have calculated the CC between the retrieved images (Figs. 6(c) and 6(d)) and the ground-truth image (the first image from the left of Fig. 4(e)), the CC values are shown in Table 1. More data on CC values under various levels of cropping and noise were presented in Fig. S3. These results indicate that the proposed method has the better robustness against the cropping and the noise than the one-step method.

To further experimentally verify the effectiveness and practicability of the proposed learning-based COA approach, we designed and set up an experiment configuration that is schematically shown in Fig. 7. A continuous-wave laser (MW-SL-532/50mW) served as the illumination source. A spatial filter and a collimating lens were placed behind the laser. A spatial light modulator (SLM) (Holoeye LC2002, transmission) was placed at the input plane to display the plaintext images. Two orthogonally oriented polarizers were placed before and after the SLM to ensure that the SLM worked in amplitude mode. A thin diffuser served as the RPM was placed next to the SLM. A high dynamic range CMOS camera (PCO edge 4.2, 2160 pixels ×2160 pixels with a pixel size of 6.5 μm × 6.5 μm, dynamic range of 16 bits) was placed on the back focal plane of the Fourier lens (f = 150 mm) to capture the power spectrum. Considering the effect of the RPM2 could be removed by the autocorrelation operation, we do not set the second RPM2 at frequency plane in the following experiments.

In the training process, 1000 images (28 pixels ×28 pixels) from the Quickdraw dataset⁶³ were selected as the plaintext samples. After scaling to the size of 100 pixels ×100 pixels, they were zero-padded to 1024 pixels ×768 pixels and loaded onto the SLM in sequence. A commercial ground glass as the training diffuser (radius r = 50 mm) rotating constantly at 1 circle per minute, which can provide about 600 different RPMs. Subsequently, 1000 corresponding power spectrum images were collected by the camera. For the test process, another 1000 images excluded from the training dataset were uploaded onto the SLM as testing plaintexts. The ground-truth plaintext images “apple” and “banana” as the representative examples of the testing images. We used three different types of diffuser (DG600, DG220 and DG120, Thorlabs) to capture the power spectrum images as testing ciphertexts. Figure 8 shows some of experimental test results. The measured power spectra images are presented in the second column from left. These three pictures appear to be obviously different although they are from the same plaintext. However, the autocorrelations of these images reveal the similar patterns. The retrieved autocorrelations from DNN1 and the retrieved plaintext images are shown in the fourth and fifth columns of Fig. 8. It is suggested that the proposed method consistently makes high-quality plaintexts retrieval from the ciphertexts of different DRPE systems. We have calculated the CC values of 1000 test data between the reconstructed plaintext images and the ground-truth images, and the average CC values of three diffusers are 0.7635, 0.6969 and 0.6214, respectively.

In summary, we have developed a two-step deep learning strategy and demonstrated numerically and experimentally that it is capable of achieving COA on the classical DRPE system. By incorporating the deep learning method with the speckle correlation technique, the proposed learning-based COA scheme employs two DNNs to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation for deciphering plaintext images. Compared with existing learning-based attack methods, the proposed method has a unique character that the mapping relationships of autocorrelation features are trained, instead of the random phase keys of DRPE system so that our approach allows to retrieve the plaintext from the only ciphertext without any other resources. Furthermore, the proposed COA method can be very efficient because the plaintext can be retrieved from the intercepted ciphertext in real-time with use of the trained DNNs. One of limitations of the proposed method is that the capacity of the generalization of de-correlation DNN model is limited, and this COA approach works well only when the test images are similar to those in the training dataset. Therefore, it should be better if the training dataset includes more types of plaintext images since the training process of two DNNs can be done before the real COA process.