EncryptGAN: Image Steganography with Domain Transform · Transform Ziqiang Zheng Ocean University...

EncryptGAN: Image Steganography with DomainTransform

Ziqiang ZhengOcean University of China

Qingdao, China

Hongzhi LiuOcean University of China

Qingdao, China

Zhibin YuOcean University of China

Qingdao, China

Haiyong ZhengOcean University of China

Qingdao, China

Yang WuNara Institute of Science and Technology

Nara, Japan

Yang YangUniversity of Electronic Science and Technology of China

Chengdu, China

Jianbo ShiUniversity of Pennsylvania

State College, US

Abstract

We propose an image steganographic algorithm called EncryptGAN, which dis-guises private image communication in an open communication channel. Theinsight is that content transform between two very different domains (e.g., face toflower) allows one to hide image messages in one domain (face) and communicateusing its counterpart in another domain (flower). The key ingredient in our method,unlike related approaches, is a specially trained network to extract transformedimages from both domains and use them as the public and private keys. We ensurethe image communication remain secret except for the intended recipient evenwhen the content transformation networks are exposed.To communicate, one directly pastes the ‘message’ image onto a larger publickey image (face). Depending on the location and content of the message image,the ‘disguise’ image (flower) alters its appearance and shape while maintainingits overall objectiveness (flower). The recipient decodes the alternated image touncover the original image message using its message image key. We implementthe entire procedure as a constrained Cycle-GAN, where the public and the privatekey generating network is used as an additional constraint to the cycle consistency.Comprehensive experimental results show our EncryptGAN outperforms the state-of-arts in terms of both encryption and security measures. Our code will beavailable at https://github.com/zhengziqiang/EncryptGAN

1 Introduction

Image steganographic techniques conceal private image message within another image. The goal isto make the concealed images appear to be something else. One approach for concealment is akinto writing with invisible ink on a ‘cover’ image. These steganographic techniques modify ‘cover’images for hiding information including spatial domain [1–6], transform domain [7–10], spread

Preprint. Under review.

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spectrum [11–15], statistic methods [16, 17], distortion techniques [18], image generation [19],element modification and adaptive steganography [20]. For many of the cases, current methods eitherleave a visible trace of the hidden image, or it is sensitive to corruption of the ‘covered’ image.

A new idea of image steganography was proposed based on domain transform [21]. The main idea isthe following. Instead of focusing on invisible inks, we will write with visible ink (private image)on visible text of one language (Cover image), and translate the combined text to another language(Disguise image). The translation process is highly nonlinear with respect to how the two writtentexts are interacting. A carefully designed reverse mapping function is used to reconstruct the originalmessage and separate out the cover image with private image.

The main weakness of this approach is that it is a symmetric encryption mechanism. The two partiesof the communication depends the security of the translation mapping functions: F for encryptionand G for decryption. Once the encryption function F and decryption function G is known, thecommunication becomes public. In addition, a symmetric image concealment method could notperform digital signature: the two communicating parties do not know if they are communicating withthe person they wish to communicate with. We build on this insight of using domain transformation asimage concealment and provides strong privacy protection using asymmetric encryption mechanism,that includes a pair of public key and private key.

The key innovative ingredient of our method is a specially learned network that transforms imagesfrom the two domains into secure public and private keys that are integrated into the encryption anddecryption learning process.

We call our asymmetric image encryption algorithm EncryptGAN, which combines adversarialtraining in a constrained Cycle-GAN framework. Imagine a situation as illustrated in Figure 1, Alicewants to send a private image (for example her cat) to Bob. First, a trusted third party pick a pair‘cover’ and ‘disguise’ images from two distinct image domains. For example, the ‘cover’ image couldbe a celebrity face, and ‘disguise’ a flower. The two images are transformed into a pair of publicand private image keys using a specially trained network. Alice is given a ‘cover‘ and transformed‘disguise’ image as Bob’s public key, Bob is given a transformed ‘cover’ image as his private key.Alice pastes in her private image (cat) into the ‘cover’ image (face) and uses Bob’s public key togenerate an altered flower image that resembles the ‘disguise’ image. Bob decrypts the receivedimage using his own private key image to recover the image of ‘face + cat’. Even when the completefunctions of encryption and decryption are leaked, we can still ensure the secrecy of the transmittedimage.

Our EncryptGAN can also achieve digital signature. If Alice needs to make sure that she is communi-cating with Bob, Bob can use the secret image as his own private key to obtain an encrypted imageand send the encrypted image to Alice. Alice decodes the encrypted image with Bob’s public key andimplements the secret matching using her own secret image. If the images do not match, Alice knowsthis information is not from Bob, hence achieving the digital signature functionality.

2 Proposed Approach

2.1 Framework

Figure 2 shows the overall design of our EncryptGAN. We build on the architecture of CycleGAN [22],including two generators: F and G, and two discriminators Dx and Dy, which are responsible fortranslating images between two domains X and Y .

To provide strong privacy protection, asymmetric encryption mechanism [23], requires a public keyand a private key for encryption and decryption.

Our main innovation is creating a specially designed network, a key generator K, for transformingimages from both domains into unrecognizable public-private key-images. In our experiment, weused a 6-layers convolutional neural network for K and constrained the output to be a 3-channelimage.

A trusted third party randomly choose two images xi and yj from two image domains X and Y , andusing K to build a private key x′

i from xi, and a public key y′j from yj .

2

Coverimage

Message

Disguise image

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Encryptedtransmittion

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Encryption Decryption

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Key distribution

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Alice Bob

Figure 1: Left: EncryptGAN asymmetric encryption image steganographic algorithm flowchart.Boxes in green denote public information, boxes in red contain private key or messages. Twodistinct image domains provide two random images including a ‘cover’ image xi and a ‘disguise’image yi. Before the communication, a third party transformed them via a learned network K intounrecognizable images: sender is given K(yi) (public) and receiver is given K(xi) (private). Senderpastes in the private message m on the ‘cover’ and using the transformed ‘disguise’ K(yi) to createan altered version of ‘disguise’ image yi(m). Receiver decodes the altered ‘disguise’ image withthe transformed ‘cover’ image K(xi) (private) back to the original private message. Right: Anintuitive explanation of our steganographic operation principle as navigation on manifolds of twoimage domains. A ‘cover’ image xi when pasted with different private messages, m1,m2, inducesmovements to distinct landmarks on its image manifold X . The Cycle-GAN generative networksensure similar movements on manifold Y relative to the ‘disguise’ image landmark. To decode theoriginal private message, it is crucial to know the original reference landmark of the ‘cover’ imagexi, because an alternative ‘cover’ image xo can also reach the same landmark location: xo +mo =xi +m2. This multiplicity of the solution provides strong privacy protection. We conjunct that K(xi)needs to encode just enough information to infer the landmark location of xi to compute m2.

To transmit a message, we use the xi as the ‘cover’ image and paste in our private message image mat a random position of xi to create a composite image. During the encryption process, the ‘cover’image is concatenated with the public key y′j to feed the encoder F for generating an encrypted imageyi(m). In the decryption stage, both the private key x′

i and the encrypted image yi(m) are used asthe input to the decoder G for decryption.

A key property we would like to demonstrate is that only the correct private key image could lead tothe correct reconstruction of the ‘cover’ image plus the message image.

2.2 Key Pairs Generation using Disentangled Representation

A deep neural network typically extracts semantic representations in higher layers and low-leveldetailed information in shallow layers [24]. By carefully choosing which level features to keepand remove in different layers, we create an irreversible process, which provides candidates for keygeneration. Following this idea, we adopt an independent convolutional neural network K as a keygenerator for our EncryptGAN to create the key pairs (public and private keys).

There are many reasons to prefer asymmetric encryption to symmetric encryption [25]. Asymmetricencryption mechanism based a public key and a private key provides an extra layer of privacyprotection for the sender and the receiver. To generate diverse key pairs for asymmetric encryption,we define a key matching loss to encourage the encoded image to have similar feature representationswith the source of the keys as follows:

Lkey(F ,G, x) =N∑i=1

Ex∼Pdata(x)[||Li(G(F(x)))− Li(x)||1 + ||Li(F(G(x)))− Li(x)||1] (1)

where Li(x) is the activation response of the ith layer from K with the input x; F(x) is the recoveredimage generated from G; N is the number of layers we need for key generation. We choose N = 3

3

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Visual imagemanifold

Representationmanifold

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MessageRandom

paste

Encryption

Decryption

T/F T/F

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Concat

Concat

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Concat

Figure 2: The network architecture of our EncryptGAN.

layers (L3,L5 and L6) in our experiments. The key matching loss aims to keep the representationsimilarity between x and G(F(x)) as well as y and F(G(y)).

2.3 Encryption and Security

One elemental requirement of an encryption mechanism is to hide information during the encryptionstage and recover the information in the decryption stage.

Information Loss. To ensure the information quality of the recovered image, we introduce theinformation loss to ‘cover’ the message information. The information loss Lloc is described as:

Linfo(F ,G) =Exinfo∼Pdata(xinfo)‖ G(F(x))− xinfo ‖1 +Eyinfo∼Pdata(y)

‖ F(G(y))− yinfo ‖1(2)

where xinfo and yinfo denote the local area including message information on both composite imageand recovered image. The information loss aims to preserve the message information in the decryptedimages.

Security is another basic requirement of an encryption mechanism. When we try to decrypt theencrypted image with an incorrect private key, we will obtain an image without correct message.Furthermore, the output images with wrong keys should not leave any cues of the ‘cover’ images,which would be the correct private key for decryption.

Security Loss. During the reconstruction process, to perform the asymmetric mechanism, we usetwo different visual images to obtain two different private keys (a true private key and a randomprivate key) to reconstruct the encrypted images. There are two possible situations during decryption.We hope the decrypted image with a true private key should recover the contents correctly, while thedecrypted image with an incorrect private key should fail to obtain the information as well as the‘cover’ image. During training, we randomly choose a correct private key and an incorrect privatekey with a probability of 50 percents, respectively. In the case of a correct private key, we use thecycle consistency loss along with the adversarial loss to increase the similarity between the recoveredimage and the composite image. Otherwise, we optimize the model only using adversarial loss. Thesecurity loss is described as:

Lscorrect(F ,G) = Lcyc + Ladv

Lsincorrect(F ,G) = Ladv

(3)

Lcyc(F ,G) =‖ G(F(x))− x ‖1 + ‖ F(G(y))− y ‖1 (4)

Ladv(F ,G) = Ex∼Pdata(x)[(D(x))− 1)2] + Ex∼Pdata(x)[(D(G(x)))2] (5)where Lscorrect and Lsincorrect

denote the security loss with a correct/incorrect private key respec-tively. Please note that the cycle-consistency loss [22] is only adopted between the recovered image

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(b)

(a)

(c)

(d)

(e)

Composite image

Publicdisguise

Encryptedimage

Coverimage

Reconstructedimage

CycleGAN

MUNIT

DRIT

EncryptGAN

Steganography

Reconstructedimage

Publicdisguise

Encryptedimage

Composite image

Figure 3: Left: The effect under different circumstances. (a) and (b) are different on publicdisguise. We choose different message digits in (a) and (c). Besides, position changes in both (a)and (d) illustrate the randomness of encrypted information location. As to (e), we achieve satisfyingencryption on different cover images. Right: Visual comparison results of different methods.

Table 1: Quantitative comparison using different methods. We don’t perform security evaluation forother methods because they are not public-key systems.

Methods FID ↓ Encryption(LPIPS ↑ / MS ↑) MSE ↓ RMSE ↓ PSNR ↑ SSIM ↑ LPIPS ↓ Security(LPIPS ↑ / MSE ↑)CycleGAN 160.9643 0.4510 / 0.1039 0.0332 0.1769 14.3246 0.9654 0.3115 -MUNIT 218.6034 0.3937 / 0.1308 0.0464 0.2122 12.6177 0.9437 0.4341 -DRIT 165.0640 0.4707 / 0.0916 0.0492 0.2111 12.9483 0.9206 0.3174 -Steganography 238.6737 0.3583 / 0.1020 0.0225 0.1426 16.1933 0.9473 0.2429 -EncryptGAN 107.9238 0.4141 / 0.0765 0.0139 0.1156 17.5992 0.9825 0.2414 0.5191 / 0.1379

and the composite image when a correct private key is given. There is no need to use consistencyloss when an incorrect private key is used because the recovered image should not be similar to theinput image at this moment. In both cases, the least square adversarial loss [26] is used to restrain thereconstructed image in the same domain as the composite image on semantic level.

3 Experiments

3.1 Implement Details

Our backbone network derives from CycleGAN Zhu et al. [22] using 9 residual blocks in thebottleneck of the generators. Our key generation K has 6 Convolution-Relu-Instancemodules, followed with a fully connected layer, and all the kernel sizes are set to 4. K is optimizedby cross-entropy loss to classify which domain the real images are from. We apply the final objectiveloss to the generator with Adam optimizer of learning rate 0.0002. Besides, we also impose someadditional noise to the input for improving the robustness of model. We conduct experiments on twoirrelevant image domains: human face images from CelebA [27] for X and flower images from 102Oxford Flower [28] for Y , and all images are resized to 256×256. For message images, we use theimages from SVHN [29] and Cat2dog [30] datasets.

We compare our EncryptGAN with available state-of-the-art methods including CycleGAN [22], MU-NIT [31], DRIT [30], and Steganography [32]. More details and results are shown in supplementaryfile for reference.

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3.2 Evaluation Metrics

Encryption performance is necessary for image steganography. To design a fair comparison, wecompute perceptual similarity using the part regions to place message cropped from encryptedand composite images (‘cover’ + message). For this, we compute Learned Perceptual Image PatchSimilarity (LPIPS [33]) and Mean Square Error (MSE) between encrypted and composite images, anddenote as Encryption(LPIPS/MSE). Higher LPIPS and MSE indexes represent better encryptionperformance and more difficult to decode the message. Besides, Fréchet Inception Distance (FID [34])computes the distance between the real sample distribution and the encrypted sample distribution.Lower FID score indicates higher image generation quality.

Decryption performance is also important to ensure how much we can decode the true message.More evaluation metrics are required to evaluate the distance between input message image andreconstructed message image (cropped from the whole image) after decryption, where we computePSNR, MSE, RMSE, SSIM and LPIPS. Lower MSE and RMSE scores show better reconstructionperformance. PSNR can roughly evaluate image quality independently, and usually higher PSNRindicates better performance, while higher SSIM represents higher structural similarity. Note, wecompute above 5 metrics between the decoded image and input image.

Security performance is necessary for encryption, to indicate how much others cannot decode thetrue message using wrong private keys. Here we compute the average difference distance betweenmessage and decoded message with other all wrong private keys generated from testing data. Note,we compute the LPIPS and MSE losses between message image and decoded message images, anddenote as Security(LPIPS/MSE). Higher difference distance represents better security performance.

3.3 Simple Information Encryption

In this section, we perform experiments using facial images cropped from CelebA (domain X) andflower images from 102 Oxford Flower (domain Y ). We aim to encrypt message images from SVHN.For this task, we also conduct experiments using CycleGAN [22], MUNIT [31], DRIT [30], andSteganography [32] in fair setting. The visual comparison results are exhibited in Figure 3. Comparedto image deep steganography [32], our method can achieve better image encryption performancevisually. Other symmetric image-to-image methods cannot reconstruct the message information,which fail to generate plausible results between two irrelevant domains. Besides, the quantitativecomparison among different methods can be found in Table 1. Our EncryptGAN obtains lowestLPIPS and highest SSIM scores, indicating best reconstruction performance. Besides, our methodcan generate more plausible images with lowest FID.

To reveal how the encryption function F hides the secret information in the encryption stage, andhow the decryption function G decodes the required information, we visualize activation responsesof each layer from two generators, and find the information is actually encrypted in the residualblocks. The visualization results are shown in Figure 4(a), we can see that the message information isencrypted at R7 block during the encryption. For the decryption stage, it is shown that we obtainthe message information at R7 block. In order to show the effectiveness of the private key, we usedifferent private keys generated from other random images to recover the encrypted image. As shownin Figure 4(b), only our specific private key can decode the required message information correctly.

We devise experiments to evaluate the key sensitivity of our EncryptGAN by adding different scalesof Gaussian noise. Here we define two cases: 1) we add noise to the visual image and generate thecorresponding private key, 2) we directly add different levels of noise to the private key. For the firstcase, we visualize the difference maps between the raw private key and corresponding private keysgenerated from images with different scales of noise. We also exhibit the reconstruction performanceat different settings. Results are shown on the left and right of Figure 4(c). As shown, if we addnoise more than N(0, 0.012), our EncryptGAN fails to reconstruct the message information for thefirst case. The difference maps show that the key generation function K is sensitive to the noiseand K magnifies the difference between samples, which can help to make the key space sparse. Forthe second case, we see that our EncryptGAN fails to decode message information if the noise isbeyond N(0, 0.012). Our model achieves high key sensitivity, and a small perturbance can changethe reconstruction output significantly. Besides, we also explore the robustness of our method byadding noise to the encrypted image in Figure 4(d), we fail to decode message from encrypted imagewith noise beyond N(0, 0.015). Finally, we also explore the effectiveness of the position to place

6

C1 C2 C3 R1 R4 R7 R9 d1 d2

K<latexit sha1_base64="Y985QFu2wTcwqPUQxaQx9TTqAKg=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgRnBTwT4gDWUynbRDJ5kwcyOUUPAn3LhQxK1f486/cdJ2oa0HBg7n3OGee8JUCoOu++2U1tY3NrfK25Wd3b39g+rhUduoTDPeYkoq3Q2p4VIkvIUCJe+mmtM4lLwTjm8Kv/PItREqecBJyoOYDhMRCUbRSn4vpjhiVOZ303615tbdGcgq8RakBgs0+9Wv3kCxLOYJMkmN8T03xSCnGgWTfFrpZYanlI3pkPuWJjTmJshnkafkzCoDEiltX4Jkpv7+kdPYmEkc2skioln2CvE/z88wug5ykaQZ8oTNF0WZJKhIcT8ZCM0ZyokllGlhsxI2opoytC1VbAne8smrpH1R99y6d39Za7Sf5nWU4QRO4Rw8uIIG3EITWsBAwTO8wpuDzovz7nzMR0vOosJj+APn8werhJH1</latexit><latexit sha1_base64="Y985QFu2wTcwqPUQxaQx9TTqAKg=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgRnBTwT4gDWUynbRDJ5kwcyOUUPAn3LhQxK1f486/cdJ2oa0HBg7n3OGee8JUCoOu++2U1tY3NrfK25Wd3b39g+rhUduoTDPeYkoq3Q2p4VIkvIUCJe+mmtM4lLwTjm8Kv/PItREqecBJyoOYDhMRCUbRSn4vpjhiVOZ303615tbdGcgq8RakBgs0+9Wv3kCxLOYJMkmN8T03xSCnGgWTfFrpZYanlI3pkPuWJjTmJshnkafkzCoDEiltX4Jkpv7+kdPYmEkc2skioln2CvE/z88wug5ykaQZ8oTNF0WZJKhIcT8ZCM0ZyokllGlhsxI2opoytC1VbAne8smrpH1R99y6d39Za7Sf5nWU4QRO4Rw8uIIG3EITWsBAwTO8wpuDzovz7nzMR0vOosJj+APn8werhJH1</latexit><latexit sha1_base64="Y985QFu2wTcwqPUQxaQx9TTqAKg=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgRnBTwT4gDWUynbRDJ5kwcyOUUPAn3LhQxK1f486/cdJ2oa0HBg7n3OGee8JUCoOu++2U1tY3NrfK25Wd3b39g+rhUduoTDPeYkoq3Q2p4VIkvIUCJe+mmtM4lLwTjm8Kv/PItREqecBJyoOYDhMRCUbRSn4vpjhiVOZ303615tbdGcgq8RakBgs0+9Wv3kCxLOYJMkmN8T03xSCnGgWTfFrpZYanlI3pkPuWJjTmJshnkafkzCoDEiltX4Jkpv7+kdPYmEkc2skioln2CvE/z88wug5ykaQZ8oTNF0WZJKhIcT8ZCM0ZyokllGlhsxI2opoytC1VbAne8smrpH1R99y6d39Za7Sf5nWU4QRO4Rw8uIIG3EITWsBAwTO8wpuDzovz7nzMR0vOosJj+APn8werhJH1</latexit><latexit sha1_base64="Y985QFu2wTcwqPUQxaQx9TTqAKg=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgRnBTwT4gDWUynbRDJ5kwcyOUUPAn3LhQxK1f486/cdJ2oa0HBg7n3OGee8JUCoOu++2U1tY3NrfK25Wd3b39g+rhUduoTDPeYkoq3Q2p4VIkvIUCJe+mmtM4lLwTjm8Kv/PItREqecBJyoOYDhMRCUbRSn4vpjhiVOZ303615tbdGcgq8RakBgs0+9Wv3kCxLOYJMkmN8T03xSCnGgWTfFrpZYanlI3pkPuWJjTmJshnkafkzCoDEiltX4Jkpv7+kdPYmEkc2skioln2CvE/z88wug5ykaQZ8oTNF0WZJKhIcT8ZCM0ZyokllGlhsxI2opoytC1VbAne8smrpH1R99y6d39Za7Sf5nWU4QRO4Rw8uIIG3EITWsBAwTO8wpuDzovz7nzMR0vOosJj+APn8werhJH1</latexit>

Concat

C1 C2 C3 R1 R4 R7 R9 d1 d2


Concat G<latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit><latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit><latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit><latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit>

Encryption

Encryptionvisualization

Decryption

Decryptionvisualization




Concat

Concat

Concat G<latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit><latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit><latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit><latexit sha1_base64="0tM4S1sDWfHztHjSTtT6PVBhBeY=">AAAB8nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgQpcV7APSUCbTSTt0kgkzN0IJBX/CjQtF3Po17vwbJ20X2npg4HDOHe65J0ylMOi6305pbX1jc6u8XdnZ3ds/qB4etY3KNOMtpqTS3ZAaLkXCWyhQ8m6qOY1DyTvh+KbwO49cG6GSB5ykPIjpMBGRYBSt5PdiiiNGZX477Vdrbt2dgawSb0FqsECzX/3qDRTLYp4gk9QY33NTDHKqUTDJp5VeZnhK2ZgOuW9pQmNugnwWeUrOrDIgkdL2JUhm6u8fOY2NmcShnSwimmWvEP/z/Ayj6yAXSZohT9h8UZRJgooU95OB0JyhnFhCmRY2K2EjqilD21LFluAtn7xK2hd1z61795e1RvtpXkcZTuAUzsGDK2jAHTShBQwUPMMrvDnovDjvzsd8tOQsKjyGP3A+fwClcJHx</latexit>



Private keys Private keys+Encrypted image

Reconstructedimage

L3 L5 L6




Concat

Concat

Concat




Withoutnoise

N(0, 0.012)<latexit sha1_base64="p2Ii8+oKglb+7sTUYfioaAfmsPY=">AAAB8XicbZDLSgMxFIbPeK31VnXpJliEClJmiqDLghtXUsFesB1KJj3ThmYyQ5IRylDwIdy4UMStb+POtzG9LLT1h8DHf0445/xBIrg2rvvtrKyurW9s5rby2zu7e/uFg8OGjlPFsM5iEatWQDUKLrFuuBHYShTSKBDYDIbXk3rzEZXmsbw3owT9iPYlDzmjxloPtyX33C27XuWsWyhamIosgzeHIsxV6xa+Or2YpRFKwwTVuu25ifEzqgxnAsf5TqoxoWxI+9i2KGmE2s+mG4/JqXV6JIyVfdKQqfv7R0YjrUdRYDsjagZ6sTYx/6u1UxNe+RmXSWpQstmgMBXExGRyPulxhcyIkQXKFLe7EjagijJjQ8rbELzFk5ehUSl7btm7uyhWG0+zOHJwDCdQAg8uoQo3UIM6MJDwDK/w5mjnxXl3PmatK848wiP4I+fzB5u3j14=</latexit><latexit sha1_base64="p2Ii8+oKglb+7sTUYfioaAfmsPY=">AAAB8XicbZDLSgMxFIbPeK31VnXpJliEClJmiqDLghtXUsFesB1KJj3ThmYyQ5IRylDwIdy4UMStb+POtzG9LLT1h8DHf0445/xBIrg2rvvtrKyurW9s5rby2zu7e/uFg8OGjlPFsM5iEatWQDUKLrFuuBHYShTSKBDYDIbXk3rzEZXmsbw3owT9iPYlDzmjxloPtyX33C27XuWsWyhamIosgzeHIsxV6xa+Or2YpRFKwwTVuu25ifEzqgxnAsf5TqoxoWxI+9i2KGmE2s+mG4/JqXV6JIyVfdKQqfv7R0YjrUdRYDsjagZ6sTYx/6u1UxNe+RmXSWpQstmgMBXExGRyPulxhcyIkQXKFLe7EjagijJjQ8rbELzFk5ehUSl7btm7uyhWG0+zOHJwDCdQAg8uoQo3UIM6MJDwDK/w5mjnxXl3PmatK848wiP4I+fzB5u3j14=</latexit><latexit sha1_base64="p2Ii8+oKglb+7sTUYfioaAfmsPY=">AAAB8XicbZDLSgMxFIbPeK31VnXpJliEClJmiqDLghtXUsFesB1KJj3ThmYyQ5IRylDwIdy4UMStb+POtzG9LLT1h8DHf0445/xBIrg2rvvtrKyurW9s5rby2zu7e/uFg8OGjlPFsM5iEatWQDUKLrFuuBHYShTSKBDYDIbXk3rzEZXmsbw3owT9iPYlDzmjxloPtyX33C27XuWsWyhamIosgzeHIsxV6xa+Or2YpRFKwwTVuu25ifEzqgxnAsf5TqoxoWxI+9i2KGmE2s+mG4/JqXV6JIyVfdKQqfv7R0YjrUdRYDsjagZ6sTYx/6u1UxNe+RmXSWpQstmgMBXExGRyPulxhcyIkQXKFLe7EjagijJjQ8rbELzFk5ehUSl7btm7uyhWG0+zOHJwDCdQAg8uoQo3UIM6MJDwDK/w5mjnxXl3PmatK848wiP4I+fzB5u3j14=</latexit><latexit sha1_base64="p2Ii8+oKglb+7sTUYfioaAfmsPY=">AAAB8XicbZDLSgMxFIbPeK31VnXpJliEClJmiqDLghtXUsFesB1KJj3ThmYyQ5IRylDwIdy4UMStb+POtzG9LLT1h8DHf0445/xBIrg2rvvtrKyurW9s5rby2zu7e/uFg8OGjlPFsM5iEatWQDUKLrFuuBHYShTSKBDYDIbXk3rzEZXmsbw3owT9iPYlDzmjxloPtyX33C27XuWsWyhamIosgzeHIsxV6xa+Or2YpRFKwwTVuu25ifEzqgxnAsf5TqoxoWxI+9i2KGmE2s+mG4/JqXV6JIyVfdKQqfv7R0YjrUdRYDsjagZ6sTYx/6u1UxNe+RmXSWpQstmgMBXExGRyPulxhcyIkQXKFLe7EjagijJjQ8rbELzFk5ehUSl7btm7uyhWG0+zOHJwDCdQAg8uoQo3UIM6MJDwDK/w5mjnxXl3PmatK848wiP4I+fzB5u3j14=</latexit>

With

Images Private keys Reconstructedimages

Difference map withraw private key

L3 L5 L6

WithN(0, 0.006)

<latexit sha1_base64="0Y87RVWTuRNQKr0QsgJkGSGQURo=">AAAB8XicbZDLSgMxFIbP1Futt6pLN8EiVJCSKaIuC25cSQV7wXYomTTThmYyQ5IRylDwIdy4UMStb+POtzHTdqGtPwQ+/nPCOef3Y8G1wfjbya2srq1v5DcLW9s7u3vF/YOmjhJFWYNGIlJtn2gmuGQNw41g7VgxEvqCtfzRdVZvPTKleSTvzThmXkgGkgecEmOth9syPsMVjC9Oe8VSBpnQMrhzKMFc9V7xq9uPaBIyaaggWndcHBsvJcpwKtik0E00iwkdkQHrWJQkZNpLpxtP0Il1+iiIlH3SoKn7+0dKQq3HoW87Q2KGerGWmf/VOokJrryUyzgxTNLZoCARyEQoOx/1uWLUiLEFQhW3uyI6JIpQY0Mq2BDcxZOXoVmtuLji3p2Xas2nWRx5OIJjKIMLl1CDG6hDAyhIeIZXeHO08+K8Ox+z1pwzj/AQ/sj5/AGgRY9h</latexit><latexit sha1_base64="0Y87RVWTuRNQKr0QsgJkGSGQURo=">AAAB8XicbZDLSgMxFIbP1Futt6pLN8EiVJCSKaIuC25cSQV7wXYomTTThmYyQ5IRylDwIdy4UMStb+POtzHTdqGtPwQ+/nPCOef3Y8G1wfjbya2srq1v5DcLW9s7u3vF/YOmjhJFWYNGIlJtn2gmuGQNw41g7VgxEvqCtfzRdVZvPTKleSTvzThmXkgGkgecEmOth9syPsMVjC9Oe8VSBpnQMrhzKMFc9V7xq9uPaBIyaaggWndcHBsvJcpwKtik0E00iwkdkQHrWJQkZNpLpxtP0Il1+iiIlH3SoKn7+0dKQq3HoW87Q2KGerGWmf/VOokJrryUyzgxTNLZoCARyEQoOx/1uWLUiLEFQhW3uyI6JIpQY0Mq2BDcxZOXoVmtuLji3p2Xas2nWRx5OIJjKIMLl1CDG6hDAyhIeIZXeHO08+K8Ox+z1pwzj/AQ/sj5/AGgRY9h</latexit><latexit sha1_base64="0Y87RVWTuRNQKr0QsgJkGSGQURo=">AAAB8XicbZDLSgMxFIbP1Futt6pLN8EiVJCSKaIuC25cSQV7wXYomTTThmYyQ5IRylDwIdy4UMStb+POtzHTdqGtPwQ+/nPCOef3Y8G1wfjbya2srq1v5DcLW9s7u3vF/YOmjhJFWYNGIlJtn2gmuGQNw41g7VgxEvqCtfzRdVZvPTKleSTvzThmXkgGkgecEmOth9syPsMVjC9Oe8VSBpnQMrhzKMFc9V7xq9uPaBIyaaggWndcHBsvJcpwKtik0E00iwkdkQHrWJQkZNpLpxtP0Il1+iiIlH3SoKn7+0dKQq3HoW87Q2KGerGWmf/VOokJrryUyzgxTNLZoCARyEQoOx/1uWLUiLEFQhW3uyI6JIpQY0Mq2BDcxZOXoVmtuLji3p2Xas2nWRx5OIJjKIMLl1CDG6hDAyhIeIZXeHO08+K8Ox+z1pwzj/AQ/sj5/AGgRY9h</latexit><latexit sha1_base64="0Y87RVWTuRNQKr0QsgJkGSGQURo=">AAAB8XicbZDLSgMxFIbP1Futt6pLN8EiVJCSKaIuC25cSQV7wXYomTTThmYyQ5IRylDwIdy4UMStb+POtzHTdqGtPwQ+/nPCOef3Y8G1wfjbya2srq1v5DcLW9s7u3vF/YOmjhJFWYNGIlJtn2gmuGQNw41g7VgxEvqCtfzRdVZvPTKleSTvzThmXkgGkgecEmOth9syPsMVjC9Oe8VSBpnQMrhzKMFc9V7xq9uPaBIyaaggWndcHBsvJcpwKtik0E00iwkdkQHrWJQkZNpLpxtP0Il1+iiIlH3SoKn7+0dKQq3HoW87Q2KGerGWmf/VOokJrryUyzgxTNLZoCARyEQoOx/1uWLUiLEFQhW3uyI6JIpQY0Mq2BDcxZOXoVmtuLji3p2Xas2nWRx5OIJjKIMLl1CDG6hDAyhIeIZXeHO08+K8Ox+z1pwzj/AQ/sj5/AGgRY9h</latexit>

(a) (b)

(c)

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Concat


Privatekey+

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Privatekey

Privatekey+



Privatekey+




Withoutnoise

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With

With

With

Reconstructedimages

Encryptedimages

Reconstructedimages

Encryptedimages





Compositeimages

Reconstructedimages

Encryptedimages

Compositeimages

Reconstructedimages

Encryptedimages

(d) (e)




Reconstructedimages

Figure 4: (a) The visual action response results of different layers from generators for both encryptionand decryption stages. (b) The effectiveness of the specific private key. (c) The exploration ofkey sensitivity of our EncryptGAN. (d) The exploration of robustness of our EncryptGAN. (e) OurEncryptGAN can encrypt message at random position.

Table 2: The quantitative comparison using different layer combinations for image encryption taskon flower↔face translation.

Methods FID ↓ Encryption(LPIPS ↑ / MSE ↑) MSE ↓ RMSE ↓ PSNR ↑ SSIM ↑ LPIPS ↓ Security(LPIPS ↑ / MSE↑)L1, L2, L3 41.1943 0.4105 / 0.1257 0.1290 0.3541 7.8553 0.8197 0.4058 0.4249 / 0.1365L3, L4, L5 210.3436 0.3259 / 0.1169 0.0159 0.1131 18.5142 0.9694 0.1438 0.7116 / 0.1132L4, L5, L6 139.7629 0.3981 / 0.0857 0.0187 0.1299 16.8173 0.9715 0.2291 0.4182 / 0.0731

L6 321.4196 0.3034 / 0.0647 0.0184 0.1337 16.3136 0.9782 0.2527 0.3634 / 0.0386L5, L6 204.7240 0.3825 / 0.0613 0.0125 0.1094 18.1165 0.9860 0.2103 0.3934 / 0.0894

L3, L5, L6(EncryptGAN) 107.9238 0.4141 / 0.0765 0.0139 0.1156 17.5992 0.9825 0.2414 0.5191 / 0.1379

the message information. For this task, we randomly place the message images at different regions,and the visual results are shown in Figure 4(e). As shown, our EncryptGAN can still reconstruct themessage information no matter where it is, indicating that our model is insensitive to the location ofthe message images. In other words, EncryptGAN is sensitive to noise while insensitive to location,that is, our method is secret yet robust with a good generalization performance.

3.3.1 Ablation Study

In this section, to investigate which combination of layers could lead to better image encryption anddecryption performance, we conduct experiments by using different layers for image steganography.Experimental results are shown in Figure 5 and Table 2. As shown in Figure 5, if we use shallowlayers such as L1, L2 and L3, the disentangled representations mainly focus on the detailed partinformation, and the model fails to reconstruct the required message information. If we only considerthe high layer representations, e.g., L6 only, we can still recover the information with a correct coverimage (private key), but fail to hide the original cover images with an incorrect key, which may leadto an exposure of private key. Thus, we achieve the best performance when we choose L3, L5 andL6, which includes both low-level and high-level information, as references to produce key pairs.

3.4 Complex Information Encryption

For this task, we aim to encrypt more complex information such as a cat or dog image. Here weresize the cat/dog images to 128× 128 and paste in them to the cover image to create a 256× 256composite image. As shown in Figure 6 (Left), if we change the public disguise image, the same

7

False coverimage

True coverimage

Encryptedimage

Publicdisguise

Compositeimage

Reconstructedimage

Reconstructedimage

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Figure 5: The ablation study of using different layer combinations for our EncryptGAN.

cover image of (a) could be encrypted to different output in (b), and if we place another messageimage to the same position, and the encrypted image could also have different expression such ascolor in (c). As for the position, if we change the position to paste in the message image, the colorand shape of encrypted image will change significantly in (d). Finally, we change the cover imageto carry the same message image used in (a) and (b), and successfully get the message image in thereconstructed image of (e). Please note that the synthesized encrypted image is also different in the3rd column of (e). From above experimental results, it is clear that our EncryptGAN works well withdifferent disguise images, different message images as well as different positions for message images.We also exhibit the visual comparison results of different methods in Figure 6 (Right).

4 Discussion and Conclusion

We proposed a domain translation framework that achieves image asymmetric encryption by com-bining the public-key cryptography system, which is the first such architecture using GAN. OurEncryptGAN can achieve a semantic level message steganography and perform better than concurrentimage-to-image translation as well as image steganographic methods. We devised ablation studies forour method and performed a comprehensive and specific analysis. Comparing with deep steganog-raphy [32], which can be considered as a symmetric encryption approach for hiding informationin a pixel level, our method hides messages in a semantic level under an asymmetric encryptionframework. A weakness of our method is that the message we wish to deliver should be alwayssmaller than the cover image. Thus, the pixel-wise message transmission rate of our model would belower than the deep steganography, which can hide messages in a full scale.

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Composite image

Publicdisguise

Encryptedimage

Coverimage

Reconstructedimage

(b)

(a)

(c)

(d)

(e)

Reconstructedimage

Publicdisguise

Encryptedimage

Composite image

CycleGAN

MUNIT

DRIT

EncryptGAN

Steganography

Figure 6: Left: The effect under different circumstances similar to Figure 3. Right: Visual compari-son results of different methods.

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