We propose a method to synthetically create or restore typical color filter array (CFA) pattern in digital images.
This can be useful, inter alia, to conceal traces of manipulation from forensic techniques that analyze the CFA
structure of images. For continuous signals, our solution maintains optimal image quality, using a quadratic
cost function; and it can be computed efficiently. Our general approach allows to derive even more efficient
approximate solutions that achieve linear complexity in the number of pixels. The effectiveness of the CFA
synthesis as tamper-hiding technique and its superior image quality is backed with experimental evidence on
large image sets and against state-of-the-art forensic techniques. This exposition is confined to the most relevant
'Bayer'-grid, but the method can be generalized to other layouts as well.
This paper revisits the steganalysis method involving a Weighted Stego-Image (WS) for estimating LSB replacement
payload sizes in digital images. It suggests new WS estimators, upgrading the method's three components:
cover pixel prediction, least-squares weighting, and bias correction. Wide-ranging experimental results (over two
million total attacks) based on images from multiple sources and pre-processing histories show that the new
methods produce greatly improved accuracy, to the extent that they outperform even the best of the structural
detectors, while avoiding their high complexity. Furthermore, specialised WS estimators can be derived
for detection of sequentially-placed payload: they offer levels of accuracy orders of magnitude better than their
Quantitative steganalysis refers to the exercise not only of detecting the presence of hidden stego messages in carrier objects, but also of estimating the secret message length. This problem is well studied, with many detectors proposed but only a sparse analysis of errors in the estimators. A deep understanding of the error model, however, is a fundamental requirement for the assessment and comparison of different detection methods. This paper presents a rationale for a two-factor model for sources of error in quantitative steganalysis, and shows evidence from a dedicated large-scale nested experimental set-up with a total of more than 200 million attacks. Apart from general findings about the distribution functions found in both classes of errors, their respective weight is determined, and implications for statistical hypothesis tests in benchmarking scenarios or regression analyses are demonstrated. The results are based on a rigorous comparison of five different detection methods under many different external conditions, such as size of the carrier, previous JPEG compression, and colour channel selection. We include analyses demonstrating the effects of local variance and cover saturation on the different sources of error, as well as presenting the case for a relative bias model for between-image error.