Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryption key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allows us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors (CPE-HCRDH) and high capacity reversible data hiding approach with embedded prediction errors (EPE-HCRDH). With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity.

Since several years, the protection of multimedia data is becoming very important. The protection of this multimedia data can be done with encryption or data hiding algorithms. To decrease the transmission time, the data compression is necessary. Since few years, a new problem is trying to combine in a single step, compression, encryption and data hiding. So far, few solutions have been proposed to combine image encryption and compression for example. Nowadays, a new challenge consists to embed data in encrypted images. Since the entropy of encrypted image is maximal, the embedding step, considered like noise, is not possible by using standard data hiding algorithms. A new idea is to apply reversible data hiding algorithms on encrypted images by wishing to remove the embedded data before the image decryption. Recent reversible data hiding methods have been proposed with high capacity, but these methods are not applicable on encrypted images. In this paper we propose an analysis of the local standard deviation of the marked encrypted images in order to remove the embedded data during the decryption step. We have applied our method on various images, and we show and analyze the obtained results.

International audienceThe quick response (QR) code was designed for storage information and high-speed reading applications. In this paper, we present a new rich QR code that has two storage levels and can be used for document authentication. This new rich QR code, named two-level QR code, has public and private storage levels. The public level is the same as the standard QR code storage level; therefore, it is readable by any classical QR code application. The private level is constructed by replacing the black modules by specific textured patterns. It consists of information encoded using q-ary code with an error correction capacity. This allows us not only to increase the storage capacity of the QR code, but also to distinguish the original document from a copy. This authentication is due to the sensitivity of the used patterns to the print-and-scan (P&S) process. The pattern recognition method that we use to read the second-level information can be used both in a private message sharing and in an authentication scenario. It is based on maximizing the correlation values between P&S degraded patterns and reference patterns. The storage capacity can be significantly improved by increasing the code alphabet q or by increasing the textured pattern size. The experimental results show a perfect restoration of private information. It also highlights the possibility of using this new rich QR code for document authentication

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This paper presents one of the first methods allowing the protection of the newly emerging video codec HEVC (High Efficiency Video Coding). Visual protection is achieved through selective encryption (SE) of HEVC-CABAC binstrings in a format compliant manner. The SE approach developed for HEVC is different from that of H.264/AVC in several aspects. Truncated rice code is introduced for binarization of quantized transform coefficients (QTCs) instead of truncated unary code. The encryption space (ES) of binstrings of truncated rice codes is not always dyadic and cannot be represented by an integer number of bits. Hence they cannot be concatenated together to create plaintext for the CFB (Cipher Feedback) mode of AES, which is a self-synchronizing stream cipher for so-called AES-CFB. Another challenge for SE in HEVC concerns the introduction of context, which is adaptive to QTC. This work presents a thorough investigation of HEVC-CABAC from an encryption standpoint. An algorithm is devised for conversion of non-dyadic ES to dyadic, which can be concatenated to form plaintext for AES-CFB. For selectively encrypted binstrings, the context of truncated rice code for binarization of future syntax elements is guaranteed to remain unchanged. Hence the encrypted bitstream is format-compliant and has exactly the same bit-rate. The proposed technique requires very little processing power and is ideal for playback on hand held devices. The proposed scheme is acceptable for DRM of a wide range of applications, since it protects the contour and motion information, along with texture. Several benchmark video sequences of different resolutions and diverse contents were used for experimental evaluation of the proposed algorithm. A detailed security analysis of the proposed scheme verified the validity of the proposed encryption scheme for content protection in a wide range of applications.

h i g h l i g h t s • Primitive extraction: detect primitive which corresponds locally to the 3D mesh. • Adjacency relation determination: define the relationship between primitives. • Wire construction: based on the intersection curves between neighboring primitives. • B-Rep creation: that works even in the case of an outline on a periodic surface.

Reversible data hiding in encrypted images (RDHEI) can be used as an effective technique to embed additional data directly in the encrypted domain and therefore, without any invasion to privacy. In this way, RDHEI is especially useful for labeling encrypted images in cloud storage. In this paper, we propose a new method of data hiding in encrypted images, which is fully reversible and has a very high payload. All the bit-planes of an image are processed recursively, from the most significant one to the least significant by combining error prediction, reversible adaptation, encryption and embedding. For pixel prediction, the Median Edge Detector, also called LOCO-I and known to be efficient in JPEG-LS compression standard, is used for each bit-plane. Moreover, conversely to current stateof-the-art methods, in our proposed method, there is no preprocessing step to correct incorrectly predicted pixels and no flags to highlight them. Indeed, a reversible adaptation of the bit-planes is performed in order to make it possible to detect and correct all incorrectly predicted pixels during the decoding step. Thanks to the high correlation between pixels in the clear domain, a large part of the bits of an image can be substituted by bits of a secret message. Our experiments show that we can generally embed bits of the secret message until the fourth mostsignificant bit-plane of an image, this allows us to have an average payload value of 2.4586 bpp.Index Terms-Image security, image encryption, reversible data hiding, recursive process, bit-plane prediction, signal processing in the encrypted domain.

Small footprint full-waveform airborne lidar systems hold large opportunities for improved forest characterisation. To take advantage of full-waveform information, this paper presents a new processing method based on the decomposition of waveforms into a sum of parametric functions. The method consists of an enhanced peak detection algorithm combined with an advanced echo modelling including Gaussian and generalized Gaussian models. The study focussed on the qualification of the extracted geometric information. Resulting 3D point clouds were compared to the point cloud provided by the operator. 40 to 60 % additional points were detected mainly in the lower part of the canopy and in the low vegetation. Their contribution to Digital Terrain Models (DTMs), Canopy Height Models (CHMs) was then analysed. The quality of DTMs and CHM-based heights was assessed using field measurements on black pine plots under various topographic and stand characteristics. Results showed only slight improvements, up to 5 cm bias and standard deviation reduction. However both tree crowns and undergrowth were more densely sampled thanks to the detection of weak and overlapping echoes, opening up opportunities to study the detailed structure of forest stands.