nils-herrmann/clean_scirepeval huggingface dataset:
Cover Selection for Steganographic Embedding

The primary goal of image steganography techniques has been to maximize embedding rate while minimizing the detectability of the resulting stego images against steganalysis techniques. However, one particular advantage of steganography, as opposed to other information hiding techniques, is that the embedder has the freedom to choose a cover image that result in the least detectable stego image. This resource has largely remained unexploited in the proposed embedding techniques. In this paper, we study the problem of cover selection by investigating three scenarios in which the embedder has either no knowledge, partial knowledge, or full knowledge of the steganalysis technique. For example, we illustrate through experiments how simple statistical measures could help embedder minimize detectability, at times by 65%, in the partial knowledge case.
An analysis of quantization based embedding-detection techniques

We analyze quantization based embedding and detection schemes in terms of the data hiding framework we have introduced (Ref.1, 2). This framework enables a better connection between analytical results and practical designs. We lay out the performance evaluation criteria and present comparison results for scalar quantization based data hiding techniques. Embedder-detector designs are evaluated based on three key issues. These are: (1) the type of post-processing employed at the embedder; (2) the form of demodulation; (3) the criteria used to optimize embedding-detection parameters. Data hiding methods are compared based on rate, correlation, and probability of error performance merits.
Fast camera fingerprint matching in very large databases

Given a query image or video, or a known camera fingerprint, there is a lack of capabilities for fast identification of media, from a large repository of images and videos, that match the query fingerprint. This work introduces a new approach that improves the computation efficiency of pairwise camera fingerprint matching and incorporates group testing to make the search more effective. More specifically, we jointly leverage the individual strengths of composite fingerprints and fingerprint digests in a novel manner and design two methods that are superior to existing approaches. The results show that under very high-performance requirements, where the probability of correct identification is close to one with a false-positive rate of zero, the proposed search methods are 2–8 times faster than the state-of-art search methods.
Sensor Fingerprint Identification Through Composite Fingerprints and Group Testing

The photo response non-uniformity noise associated with an imaging sensor has been shown to be a unique and persistent identifier that can be treated as the sensor's digital fingerprint. The method for attributing an image to a particular camera, however, is not suitable for source identification due to efficiency considerations, which is a one-to-many matching of a single fingerprint against a database of fingerprints. To address this problem, we propose a group-testing approach based on the notion of composite fingerprints (CFs), generated by combining many actual fingerprints together into a single fingerprint. Our technique organizes a database of fingerprints into an unordered binary search tree, wherein each internal node is represented by a fingerprint composited from all the fingerprints at the leaf nodes in the subtree beneath that node. Different search strategies are considered, and the performance is analyzed analytically and verified using numerical simulations as well as experimental results. Our results are presented in comparison with the linear search-based approach that utilizes fingerprint digests for more effective computation. Results obtained under the best achievable accuracy showed that the proposed method yields a lower overall computational cost. It is also shown that by complementary use of the fingerprint dimension reduction and CF-based search tree approaches, it is possible to further improve the search efficiency.
Towards automatic detection of child pornography

This paper presents a child pornographic image detection system that identifies human skin tones in digital images, extracts features to detect explicit images and performs facial image based age classification. The novelty of the technique relies on the use of a robust and very fast skin color filter and a new set of facial features for improved identification of child faces. Tests on a dataset containing explicit images taken under different illuminations and reflecting a diversity of human skin tones, show that explicit images can be differentiated from benign images with around 90% accuracy. Similarly, tests performed on adult and child facial images yielded an accuracy of 80% in detecting child faces. Test conducted on 105 images involving semi-naked children (with no sexual context) revealed that the system has true positive rates of 83% in detecting explicit-like images and 96.5% in detecting child faces.
A new perspective for embedding-detection methods with distortion compensation and thresholding processing techniques

In this paper, we analyze oblivious (blind) information hiding methods from a new perspective. In 1983, Costa introduced a communications framework that also applies to oblivious information hiding. We present an alternate and equivalent framework by carrying out the channel dependent nature of the optimal encoder in a different manner. Within the proposed framework, decoder structure is simplified, although in effect it's a slender advantage compared to overall complexity. This interpretation provides a better connection between the analytical results and practical designs. We evaluate the practical embedding-detection schemes employing scalar quantization procedures along with thresholding and distortion compensation types of processings from this perspective. Furthermore, we justify the assumptions for the optimality of the two types of processings.
Volleystore: A Parasitic Storage Framework

We present Volleystore, a filesystem that stores data on network equipment and servers without any authorization, yet without compromising the systems that are used. This is achieved by exploiting the echo functionality present in most standard Internet protocols. Various issues concerning the design of a parasitic storage system are addressed and a practical system based on Internet Control Message Protocol (ICMP) echo messages is demonstrated. We present an analysis of storage capacity and latency limits for various configurations of the system. We also describe a proof-of-concept implementation of the system and show that one can indeed store data using Volleystore for a reasonable lengths of time. Finally, we suggest defenses against parasitic storage, both abstractly in economic terms and concretely in technical terms.
What is biometric information and how to measure it?

Being able to measure the information content of biometric data has significant importance. However, it is not only difficult to define the biometric information conceptually, but also a challenging estimation problem due to many practical issue such as, different feature representation of different biometric modalities, unknown feature distributions, limited sample size, etc. In this paper, we provided a summary of the several proposed approaches and discuss their merits and shortcomings of these works and focused on techniques which are based on relative entropy. We also introduced a user-specific biometric information measure and evaluated these measures using iris biometric. We explored the effect of the biometric sample quality on the biometric information measurements using different subsets of the iris data with different quality levels. We also investigated the effect of binarization/encoding phase in the iris recognition on the biometric information by comparing our results obtained from image and binary feature domain.
How to Measure Biometric Information?

Being able to measure the actual information content of biometrics is very important but also a challenging problem. Main difficulty here is not only related to the selected feature representation of the biometric data, but also related to the matching algorithm employed in biometric systems. In this paper, we propose a new measure for measuring biometric information using relative entropy between intra-user and inter-user distance distributions. As an example, we evaluated the proposed measure on a face image dataset.
Classification of audios containing speech and music

We propose an automated technique that uses perceptual and non-perceptual audio quality measures for discrimination of speech and music signals with high accuracy. Deployed audio quality measures used for characterization of audio are obtained via de-noising of the original audio. The underlying idea of the approach is that de-noising operation affects speech and music signals in a different and consistent manner and these differences can be captured by the audio quality metrics. Obtained quality measures are then used in conjunction with a machine learning classifier to statistically model speech and music signals. To determine the accuracy of the proposed method, tests have been performed on different datasets with and without audio compression.
An Ensemble of Classifiers Approach to Steganalysis

Most work on steganalysis, except a few exceptions, have primarily focused on providing features with high discrimination power without giving due consideration to issues concerning practical deployment of steganalysis methods. In this work, we focus on machine learning aspect of steganalyzer design and utilize a hierarchical ensemble of classifiers based approach to tackle two main issues. Firstly, proposed approach provides a workable and systematic procedure to incorporate several steganalyzers together in a composite steganalyzer to improve detection performance in a scalable and cost-effective manner. Secondly, since the approach can be readily extended to multi-class classification it can also be used to infer the steganographic technique deployed in generation of a stego-object. We provide results to demonstrate the potential of the proposed approach.
Efficient Sensor Fingerprint Matching Through Fingerprint Binarization

It is now established that photo-response nonuniformity noise pattern can be reliably used as a fingerprint to identify an image sensor. The large size and random nature of sensor fingerprints, however, make them inconvenient to store. Further, associated fingerprint matching method can be computationally expensive, especially for applications that involve large-scale databases. To address these limitations, we propose to represent sensor fingerprints in binary-quantized form. It is shown through both analytical study and simulations that the reduction in matching accuracy due to quantization is insignificant as compared to conventional approaches. Experiments on actual sensor fingerprint data are conducted to confirm that only a slight increase occurred in the probability of error and to demonstrate the computational efficacy of the approach.
Methods for identifying traces of compression in audio

Compression history of an audio may reveal very useful information when traces of tampering has to be investigated or quality of an audio has to be evaluated. Motivated by this, we introduce two methods that can discriminate between single and double compressed audio and can identify compression codec and bit rate of an audio. The first method utilizes audio quality measures to realize this and operates on decoded audio. The second method, alternatively, works on coded audio, effectively the audio bit stream, and characterizes randomness and chaotic properties of the bit stream to achieve these tasks. Unlike the existing work in the literature, which are proposed mainly for MP3 encoded audio, both methods can be applied to all encoding formats. Extensive tests have been performed to test the performance of both methods under various settings. Results show that both methods can be very reliably used to obtain information on compression history of an audio.
Tamper Detection Based on Regularity of Wavelet Transform Coefficients

Powerful digital media editing tools make producing good quality forgeries very easy for almost anyone. Therefore, proving the authenticity and integrity of digital media becomes increasingly important. In this work, we propose a simple method to detect image tampering operations that involve sharpness/blurriness adjustment. Our approach is based on the assumption that if a digital image undergoes a copy-paste type of forgery, average sharpness/blurriness value of the forged region is expected to be different as compared to the non-tampered parts of the image. The method of estimating sharpness/blurriness value of an image is based on the regularity properties of wavelet transform coefficients which involves measuring the decay of wavelet transform coefficients across scales. Our preliminary results show that the estimated sharpness/blurriness scores can be used to identify tampered areas of the image.
Blind source camera identification

An interesting problem in digital forensics is that given a digital image, would it be possible to identify the camera model which was used to obtain the image. In this paper we look at a simplified version of this problem by trying to distinguish between images captured by a limited number of camera models. We propose a number of features which could be used by a classifier to identify the source camera of an image in a blind manner. We also provide experimental results and show reasonable accuracy in distinguishing images from the two and five different camera models using the proposed features.
Preprocessing tool for compressed video editing

This paper presents a simple and effective preprocessing method developed for editing compressed video sequences. The proposed method involves extracting information about different video segments from the compressed bitstream. The algorithm is not designed to distinguish among types of segments but rather to indicate the position and duration. Since no decoding of the bitstream is done, the computational load of the algorithm is very low. Although the experimental results are shown on MPEG compressed video sequences, the algorithm can easily be applied to MJPEG, MPEG4 sequences given the header information.
Combatting Ambiguity Attacks via Selective Detection of Embedded Watermarks

This paper focuses on a problem that is common to most watermarking-based ownership dispute resolutions and ownership assertion systems. Such systems are vulnerable to a simple but effective class of attacks that exploit the high false-positive rate of the watermarking techniques to cast doubt on the reliability of a resulting decision. To mitigate this vulnerability, we propose embedding multiple watermarks, as opposed to embedding a single watermark, and detecting a randomly selected subset of them while constraining the embedding distortion. The crux of the scheme lies in both watermark generation, which deploys a family of one-way functions and selective detection, which injects uncertainty into the detection process. The potential of this approach in reducing the false-positive probability is analyzed under various operating conditions and compared to single watermark embedding. The multiple watermark embedding and selective detection technique is incorporated analytically into the additive watermarking technique and results obtained through numerical solutions are presented to illustrate its effectiveness.
Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust

Digital single lens reflex cameras suffer from a well-known sensor dust problem due to interchangeable lenses that they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust-spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter-dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex camera at low false positive rates, even under heavy compression and downsampling.
Source camera identification based on CFA interpolation

In this work, we focus our interest on blind source camera identification problem by extending our results in the direction of M. Kharrazi et al. (2004). The interpolation in the color surface of an image due to the use of a color filter array (CFA) forms the basis of the paper. We propose to identify the source camera of an image based on traces of the proprietary interpolation algorithm deployed by a digital camera. For this purpose, a set of image characteristics are defined and then used in conjunction with a support vector machine based multi-class classifier to determine the originating digital camera. We also provide initial results on identifying source among two and three digital cameras.
Carving Orphaned JPEG File Fragments

File carving techniques allow for recovery of files from storage devices in the absence of any file system metadata. When data are encoded and compressed, the current paradigm of carving requires the knowledge of the compression and encoding settings to succeed. In this paper, we advance the state of the art in JPEG file carving by introducing the ability to recover fragments of a JPEG file when the associated file header is missing. To realize this, we examined JPEG file headers of a large number of images collected from Flickr photo sharing site to identify their structural characteristics. Our carving approach utilizes this information in a new technique that performs two tasks. First, it decompresses the incomplete file data to obtain a spatial domain representation. Second, it determines the spatial domain parameters to produce a perceptually meaningful image. Recovery results on a variety of JPEG file fragments show that given the knowledge of Huffman code tables, our technique can very reliably identify the remaining decoder settings for all fragments of size 4 KiB or above. Although errors due to detection of image width, placement of image blocks, and color and brightness adjustments can occur, these errors reduce significantly when fragment sizes are >32 KiB.
An efficient and robust method for detecting copy-move forgery

Copy-move forgery is a specific type of image tampering, where a part of the image is copied and pasted on another part of the same image. In this paper, we propose a new approach for detecting copy-move forgery in digital images, which is considerably more robust to lossy compression, scaling and rotation type of manipulations. Also, to improve the computational complexity in detecting the duplicated image regions, we propose to use the notion of counting bloom filters as an alternative to lexicographic sorting, which is a common component of most of the proposed copy-move forgery detection schemes. Our experimental results show that the proposed features can detect duplicated region in the images very accurately, even when the copied region was undergone severe image manipulations. In addition, it is observed that use of counting bloom filters offers a considerable improvement in time efficiency at the expense of a slight reduction in the robustness.
Improvements on Sensor Noise Based Source Camera Identification

In a novel method for identifying the source camera of a digital image is proposed. The method is based on first extracting imaging sensor's pattern noise from many images and later verifying its presence in a given image through a correlative procedure. In this paper, we investigate the performance of this method in a more realistic setting and provide results concerning its detection performance. To improve the applicability of the method as a forensic tool, we propose an enhancement over it by also verifying that class properties of the image in question are in agreement with those of the camera. For this purpose, we identify and compare characteristics due to demosaicing operation. Our results show that the enhanced method offers a significant improvement in the performance.
New Features to Identify Computer Generated Images

Discrimination of computer generated images from real images is becoming more and more important. In this paper, we propose the use of new features to distinguish computer generated images from real images. The proposed features are based on the differences in the acquisition process of images. More specifically, traces of demosaicking and chromatic aberration are used to differentiate computer generated images from digital camera images. It is observed that the former features perform very well on high quality images, whereas the latter features perform consistently across a wide range of compression values. The experimental results show that proposed features are capable of improving the accuracy of the state-of-the-art techniques.
Flatbed scanner identification based on dust and scratches over scanner platen

In this paper, a novel individual source scanner identification scheme is proposed. The scheme uses traces of dust, dirt, and scratches over scanner platen on scanned images to characterize a source scanner. The efficacy of the proposed scheme is substantiated with experimental analysis. The robustness of the scheme to the JPEG compression is also investigated. Experimental results show that proposed scheme could be used to match a scanned image to its source.
A robust Type-III data hiding technique against cropping and resizing attacks

Proposes an oblivious information hiding method which allows watermark recovery for signals subjected to cropping and resampling consecutively. We employ multiple embedding of a watermark signal using a Type-III data hiding method which has a better robustness vs. rate trade off than the conventional methods under mean square error distortion measure. Cyclic autocorrelation features of the cropped-resampled signal are used to estimate the amount of cropping. We analytically showed that within a small error range, it is possible to restore the cropped-resampled signal to the cropped signal. Synchronization of watermark detection in the cropped stego-signal is achieved by designing white noise like watermark signals that are uncorrelated with their shifted replicas. For this purpose we use all-pass filters which are orthogonal to all their cyclic shifts. Freedom to hide data is obtained by modulating the phase of the cyclic all-pass filters. Further, we use Reed-Solomon error correcting codes both for introducing redundancy and achieving synchronization. We also address the issue of data hiding under multiple cropping attacks.
PSTEG: steganographic embedding through patching [image steganography]

In this paper, we propose a novel approach to image steganography in which embedding is done without making explicit modifications to the image; that is, the embedding distortion introduced to the cover image is both perceptually and statistically ensured to be less detectable. If an image is divided into blocks and each block is hashed, then the hash values could represent the embedded message content. A set of replacement blocks can be obtained by consecutively capturing images of the same scene (or resampling the incident light). Since image content remains the same and noise is sampled, the set of replacements are statistically compatible while still providing unique hash values. With such an approach, the embedder can choose the block with hash value corresponding to the message content without violating any of the natural image statistics. When applied to JPEG images, experiments show that this technique can achieve embedding rates of 0.063 bits per DCT coefficient or 0.157 bits per nonzero DCT coefficient while still remaining undetectable by Farid's universal steganalysis tool.