Digital multimedia such as images and videos are prevalent on today's internet and cause significant
social impact, which can be evidenced by the proliferation of social networking sites with user generated
contents. Due to the ease of generating and modifying images and videos, it is critical to establish
trustworthiness for online multimedia information. In this paper, we propose novel approaches to
perform multimedia forensics using compact side information to reconstruct the processing history of
a document. We refer to this as FASHION, standing for Forensic hASH for informatION assurance.
Based on the Radon transform and scale space theory, the proposed forensic hash is compact and
can effectively estimate the parameters of geometric transforms and detect local tampering that an
image may have undergone. Forensic hash is designed to answer a broader range of questions regarding
the processing history of multimedia data than the simple binary decision from traditional robust
image hashing, and also offers more efficient and accurate forensic analysis than multimedia forensic
techniques that do not use any side information.
The popularity of video sharing platforms such as Youtube has prompted the need for the development of efficient
techniques for multimedia identification. Content fingerprinting is a promising solution for this problem, whereby
a short "fingerprint" that captures robust and unique characteristics of a signal is computed from each multimedia
document. This fingerprint is then compared with a database to identify the multimedia. Several fingerprinting
techniques have been proposed in the literature and have been evaluated using experiments. To complement
these experimental evaluations and gain a deeper understanding, this paper proposes a framework for theoretical
modeling and analysis of content fingerprinting schemes. Analysis of some key modules for fingerprint encoding
and matching are also presented under this framework.
Performing information retrieval tasks while preserving data confidentiality is a desirable capability when a
database is stored on a server maintained by a third-party service provider. This paper addresses the problem
of enabling content-based retrieval over encrypted multimedia databases. Search indexes, along with multimedia
documents, are first encrypted by the content owner and then stored onto the server. Through jointly applying
cryptographic techniques, such as order preserving encryption and randomized hash functions, with image
processing and information retrieval techniques, secure indexing schemes are designed to provide both privacy
protection and rank-ordered search capability. Retrieval results on an encrypted color image database and security
analysis of the secure indexing schemes under different attack models show that data confidentiality can
be preserved while retaining very good retrieval performance. This work has promising applications in secure
With sophisticated video editing technologies, it is becoming increasingly easy to tamper digital video without
leaving visual clues. One of the common tampering operations on video is to remove some frames and then
re-encode the resulting video. In this paper, we propose a new method for detecting this type of tampering by
exploring the temporal patterns of the block artifacts in video sequences. We show that MPEG compression
introduces different block artifacts into various types of frames and that the strength of the block artifacts as
a function over time has a regular pattern for a given group of pictures (GOP) structure. When some frames
are removed from an MPEG video file and the file is then recompressed, the block artifacts introduced by the
previous compression would remain and affect the average of block artifact strength of the recompressed one in
such a way that depends on the number of deleted frames and the type of GOP used previously. We propose a
feature curve to reveal the compression history of an MPEG video file with a given GOP structure, and use it as
evidence to detect tampering. Experimental results evaluated on common video benchmark clips demonstrate
the effectiveness of the proposed method.
A large portion of digital image data available today is acquired using digital cameras or scanners. While cameras
allow digital reproduction of natural scenes, scanners are often used to capture hardcopy art in more controlled
scenarios. This paper proposes a new technique for non-intrusive scanner model identification, which can be
further extended to perform tampering detection on scanned images. Using only scanned image samples that
contain arbitrary content, we construct a robust scanner identifier to determine the brand/model of the scanner
used to capture each scanned image. The proposed scanner identifier is based on statistical features of scanning
noise. We first analyze scanning noise from several angles, including through image de-noising, wavelet analysis,
and neighborhood prediction, and then obtain statistical features from each characterization. Experimental
results demonstrate that the proposed method can effectively identify the correct scanner brands/models with
With growing popularity of digital imaging devices and low-cost image editing software, the integrity of image
content can no longer be taken for granted. This paper introduces a methodology for forensic analysis of
digital camera images, based on the observation that many in-camera and post-camera processing operations
leave distinct traces on digital images. We present methods to identify these intrinsic fingerprint traces of
the various processing operations and employ them to verify the authenticity of digital data. We develop
an explicit imaging model to characterize the properties that should be satisfied by a direct camera output,
and model any further processing applied to the camera captured image by a manipulation filter. Utilizing
the manipulation filter coefficients and reference patterns estimated from direct camera outputs using blind
deconvolution techniques, the proposed methods are capable of detecting manipulations made by previously
unseen operations and steganographic embedding.
Digital fingerprinting is an emerging technology to protect multimedia from unauthorized use by embedding a unique fingerprint signal into each user's copy. A robust embedding algorithm is an important building block in order to make the fingerprint resilient to various distortions and collusion attacks. Spread spectrum embedding has been widely used for multimedia fingerprinting. In this paper, we explore another class of embedding methods - Quantization Index Modulation (QIM) for fingerprinting applications. We first employ Dither Modulation (DM) technique and extend it for embedding multiple symbols through a basic dither sequence design. We then develop a theoretical model and propose a new algorithm to improve the collusion resistance of the basic scheme. Simulation results show that the improvement algorithm enhances the collusion resistance, while there is still a performance gap with the existing spread spectrum based fingerprinting. We then explore coded fingerprinting based on spread transform dither modulation (STDM) embedding. Simulation results show that this coded STDM based fingerprinting has significant advantages over spread spectrum based fingerprinting under blind detection.
Digital elevation maps (DEMs) provide a digital representation of 3-D terrain information. In civilian applications, high-precision DEMs carry a high commercial value owing to the large amount of effort in acquiring them; and in military applications, DEMs are often used to represent critical geospatial information in sensitive operations. These call for new technologies to prevent unauthorized distribution and to trace traitors in the event of information leak related to DEMs. In this paper, we propose a new digital fingerprinting technique to protect DEM data from illegal re-distribution. The proposed method enables reliable detection of fingerprints from both 3-D DEM data set and its 2-D rendering, whichever format that is available to a detector. Our method starts with extracting from a DEM a set of critical contours either corresponding to important topographic features of the terrain or having application-dependent importance. Fingerprints are then embedded into these critical contours by employing parametric curve modeling and spread spectrum embedding. Finally, a fingerprinted DEM is constructed to incorporate the marked 2-D contours. Through experimental results, we demonstrate the robustness of the proposed method against a number of challenging attacks applied to either DEMs or their contour representations.
This paper proposes a group-based fingerprinting scheme employing
a joint coding and embedding strategy to trace multimedia distribution and proactively prevent the leak of multimedia information. Taking advantage of the prior knowledge on the collusion pattern, we construct compact fingerprints that consist
of user sub-codeword and group sub-codeword and are embedded in host signal via spread spectrum technique. The detection is done in two levels, which identifies guilty groups through correlation and then narrows down to specific colluders through minimum distance decoding. Experimental results show that the proposed method provides higher collusion resistance than the existing non-grouped fingerprint codes.
Hiding data in binary images can facilitate the authentication and annotation of important document images in digital domain. A representative approach is to first identify pixels whose binary color can be flipped without introducing noticeable artifacts, and then embed one bit in each non-overlapping block by adjusting the flippable pixel values to obtain the desired block parity. The distribution of these flippable pixels is highly uneven across the image, which is handled by random shuffling in the literature. In this paper, we revisit the problem of data embedding for binary images and investigate the incorporation of a most recent steganography framework known as <i>the wet paper coding</i> to improve the embedding capacity. The wet paper codes naturally handle the uneven embedding capacity through randomized projections. In contrast to the previous approach, where only a small portion of the flippable pixels are actually utilized in the embedding, the wet paper codes allow for a high utilization of pixels that have high flippability score for embedding, thus giving a significantly improved embedding capacity than the previous approach. The performance of the proposed technique is demonstrated on several representative images. We also analyze the perceptual impact and capacity-robustness relation of the new approach.
Data embedding mechanism used for authentication applications should be secure in order to prevent an adversary from forging the embedded data at his/her will. Meanwhile, semi-fragileness is often preferred to allow for distinguishing content changes versus non-content changes. In this paper, we focus on jointly enhancing the robustness and security of the embedding mechanism, which can be used as a building block for authentication. The paper presents analysis showing that embedding through a look-up table (LUT) of
non-trivial run that maps quantized multimedia features randomly to binary data offers a probability of detection error considerably smaller than that of the traditional quantization embedding. We quantify the security strength of LUT embedding and enhance its robustness through distortion compensation. We introduce a combined security and capacity measure and show that the proposed distortion compensated LUT embedding provides joint enhancement of security and robustness over the traditional quantization embedding.
Digital fingerprints are unique labels inserted in different copies of the same content before distribution. Each digital fingerprint is assigned to an inteded recipient, and can be used to trace the culprits who use their content for unintended purposes. Attacks mounted by multiple users, known as collusion attacks, provide a cost-effective method for attenuating the identifying fingerprint from each coluder, thus collusion poses a reeal challenge to protect the digital media data and enforce usage policies. This paper examines a few major design methodologies for collusion-resistant fingerprinting of multimedia, and presents a unified framework that helps highlight the common issues and the uniqueness of different fingerprinting techniques.
In network delivery of compressed video, packets may be lost if the channel is unreliable. Such losses tend to occur in burst. In this paper, we develop an error resilient video encoding approach to help error concealment at the decoder. We introduce a new block shuffling scheme to isolate erroneous blocks caused by packet losses. And we apply data hiding to add additional protection for motion vectors. The incorporation of these scheme adds little complexity to the standard encoder. Experimental results suggest that our approach can achieve a reasonable quality for packet loss up to 30% over a wide range of video materials.
Many multimedia data hiding systems demand either multiple bits or multiple sets of data to be embedded. This paper examines the modulation and multiplexing techniques for accomplishing the task of extending the basic single-bit embedding to multiple-bit embedding. Amplitude modulo modulation, orthogonal/bi-orthogonal modulation, and TDMA and CDMA type modulation/multiplexing are discussed and compared. Several examples are included to demonstrate the use of such techniques in practical designs.
Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example, simple rotation, scaling, and/or translation (RST) of an image can prevent detection of a public watermark. In this paper, we propose a watermarking algorithm that is robust to RST distortions. The watermark is embedded into a 1-dimensional signal obtained by first taking the Fourier transform of the image, resampling the Fourier magnitudes into log-polar coordinates, and then summing a function of those magnitudes along the log-radius axis. If the image is rotated, the resulting signal is cyclically shifted. If it is scaled, the signal is multiplied by some value. And if the image is translated, the signal is unaffected. We can therefore compensate for rotation with a simple search, and for scaling by using the correlation coefficient for the detection metric. False positive results on a database of 10,000 images are reported. Robustness results on a database of 2,000 images are described. It is shown that the watermark is robust to rotation, scale and translation. In addition, the algorithm shows resistance to cropping.
Previous works on data hiding generally targeted on a specific tradeoff between capacity and robustness. This results in overestimation of the processing noise under some situations and/or underestimation under some other situations, hence limits the overall performance. In this paper, we propose a multi-level data hiding scheme which is able to convey secondary data in high rate when noise is not severe and can also convey some data reliably under heavy distortion. The proposed scheme is motivated by a two- category classification of embedding schemes and by a study on detection performance of spread spectrum watermarking. The multi- level data hiding has been successfully applied to both digital image and video, and can be used for applications such as copy control.