Simone Milani

Discriminating multiple JPEG compression using first digit features

The analysis of double-compressed images is a problem largely studied by the multimedia forensics community, as it might be exploited, e.g., for tampering localization or source device identification. In many practical scenarios, e.g. photos uploaded on blogs, on-line albums, and photo sharing Web sites, images might be compressed several times. However, the identification of the number of compression stages applied to an image remains an open issue. This paper proposes a forensic method based on the analysis of the distribution of the first significant digits of DCT coefficients, which is modeled according to Benfords law. The method relies on a set of Support Vector Machine (SVM) classifiers and allows us to accurately identify the number of compression stages applied to an image. Up to four consecutive compression stages were considered in the experimental validation. The proposed approach extends and outperforms the previously published methods aimed at detecting double JPEG compression.

Video codec identification

Video content is routinely acquired and distributed in digital format. Therefore, it is customary to have the content encoded multiple times. In this paper we consider a processing chain of two coding steps and we propose a method that aims at identifying the type of codec used in the first step, by analyzing its coding-based footprints. The method relies on the fact that lossy coding is an almost idempotent operation, i.e., re-encoding the reconstructed sequence with the same codec and coding parameters produces a sequence that is highly correlated with the input one. As a consequence, it is possible to analyze this sort of correlation to identify the first codec provided that the second codec does not introduce severe quality degradation. The proposed solution finds several applications in the field of multi-media forensics, e.g. to identify the device that generated the original video stream or detect collages of different sequences.

Multiple compression detection for video sequences

Nowadays, thanks to the increasingly availability of powerful processors and user friendly applications, the editing of video sequences is becoming more and more frequent. Moreover, after each editing step, any video object is almost always encoded in order to store it using a less amount of memory. For this reason, inferring the number of compression steps that have been applied to such a multimedia object is an important clue in order to assess its authenticity. In this paper we propose a method to recover the number of compression steps applied to a video sequence. In order to accomplish this goal, we make use of a classifier based on multiple Support Vector Machines (SVM) exploiting the Benfords law. Indeed, the feature vectors used to train and test the SVM are based on the statistics of the most significant digit of quantized transform coefficients. The proposed method is tested with a generic hybrid video encoder combining motion-compensation and block coding. Results show that this method is able to discriminate up to three compression stages with high accuracy.

A Depth Image Coder Based on Progressive Silhouettes

An efficient compression of depth maps proves to be a crucial element in the transmission and storage of 3-D scenes. However, the peculiarities of geometry information make the traditional coding paradigms for natural images less effective for the coding of depth images. The letter presents a novel coding scheme that employs an oversegmentation of the input depth image into a huge set of small regions. These regions are then fused together according to the target number of objects that the algorithm needs to identify in the representation. This procedure is iterated more than once generating several refinement layers that permit obtaining a progressively-increasing quality in the scene. Experimental results show that in most cases the proposed approach reaches a better coding performance with respect to previous coding methods.

Local tampering detection in video sequences

Video sequences are often believed to provide stronger forensic evidence than still images, e.g., when used in lawsuits. However, a wide set of powerful and easy-to-use video authoring tools is today available to anyone. Therefore, it is possible for an attacker to maliciously forge a video sequence, e.g., by removing or inserting an object in a scene. These forms of manipulation can be performed with different techniques. For example, a portion of the original video may be replaced by either a still image repeated in time or, in more complex cases, by a video sequence. Moreover, the attacker might use as source data either a spatio-temporal region of the same video, or a region taken from an external sequence. In this paper we present the analysis of the footprints left when tampering with a video sequence, and propose a detection algorithm that allows a forensic analyst to reveal video forgeries and localize them in the spatio-temporal domain. With respect to the state-of-the-art, the proposed method is completely unsupervised and proves to be robust to compression. The algorithm is validated against a dataset of forged videos available online.

Performance evaluation of the 1st and 2nd generation Kinect for multimedia applications

Microsoft Kinect had a key role in the development of consumer depth sensors being the device that brought depth acquisition to the mass market. Despite the success of this sensor, with the introduction of the second generation, Microsoft has completely changed the technology behind the sensor from structured light to Time-Of-Flight. This paper presents a comparison of the data provided by the first and second generation Kinect in order to explain the achievements that have been obtained with the switch of technology. After an accurate analysis of the accuracy of the two sensors under different conditions, two sample applications, i.e., 3D reconstruction and people tracking, are presented and used to compare the performance of the two sensors.

Efficient depth map compression exploiting segmented color data

3D video representations usually associate to each view a depth map with the corresponding geometric information. Many compression schemes have been proposed for multi-view video and for depth data, but the exploitation of the correlation between the two representations to enhance compression performances is still an open research issue. This paper presents a novel compression scheme that exploits a segmentation of the color data to predict the shape of the different surfaces in the depth map. Then each segment is approximated with a parameterized plane. In case the approximation is sufficiently accurate for the target bit rate, the surface coefficients are compressed and transmitted. Otherwise, the region is coded using a standard H.264/AVC Intra coder. Experimental results show that the proposed scheme permits to outperformthe standardH.264/AVC Intra codec on depth data and can be effectively included into multi-view plus depth compression schemes.

An Accurate Low-Complexity Rate Control Algorithm Based on

The standard H.264/AVC defines an efficient coding architecture both for coding applications where bandwidth or storage capacity is limited (e.g., video telephony or video conferencing over mobile channels and devices) and for applications that require high reconstruction quality and bit rate (e.g., HDTV). Since its main applications concern video communication over time-varying bandwidth channels, the bit rate has to be controlled with scalable algorithms that can be implemented on low resource devices. The paper describes a rate control algorithm that needs reduced memory area and complexity compared to other ones. The number of coded bits for each frame is accurately predicted through the percentage of null quantized transform coefficients, which is related to the quantization step via the energy of the quantized signal. It is possible to design a rate control algorithm based on this model that provides a good compression performance at a low computational cost.

(\rho, E_{q})

Infrared structured light sensors are widely employed for control applications, gaming, acquisition of dynamic and static 3D scenes. Recent developments have lead to the availability on the market of low-cost sensors which prove to be extremely sensitive to noise, light conditions, materials, the surface nature of the objects, and their distance from the camera. As a matter of fact, accurate denoising and interpolation strategies are needed. The paper presents a quality enhancement strategy for depth maps targeting low-cost IR structured light sensors. The approach has been tested using the MS Xbox Kinect device in both indoor and outdoor scenarios under different light conditions.

-Domain

Image tampering is nowadays at everyones reach. This has determined an urgent need of tools capable of revealing such alterations. Unfortunately, while forgeries can be operated in many different ways, forensic tools usually focus on one specific kind of forgeries. Therefore, an effective strategy for tampering detection and localization requires to merge the output of many different forensic tools. In this paper, we propose an algorithm for image tampering localization, based on the fusion of three separate detectors: i) one based on PRNU, working when we have at least a few of pictures shot with the same camera; ii) one based on PatchMatch; iii) one exploiting image phylogeny analysis, in case we have a set of near-duplicate images to analyze. The method is validated against the dataset released by the IEEE Information Forensics and Security Technical Committee for the First Image Forensics Challenge. Results show that the proposed algorithm can beat the challenge with the highest score achieved at paper submission time.