We evaluate an implementation of our system against multiple typefaces, font sizes, grid sizes, pixel offsets, and levels of noise. Here we use HMMs in an analogous way to recover sequences of characters from images of redacted text. Our approach borrows on the success of HMMs for automatic speech recognition, where they are used to recover sequences of phonemes from utterances of speech. Our main finding is that we can use a simple but powerful class of statistical models - so-called hidden Markov models (HMMs) - to recover both short and indefinitely long instances of redacted text. We consider the effectiveness of two popular image transforms - mosaicing (also known as pixelization) and blurring - for redaction of text. Sometimes solid bars are used sometimes a blur or other image transform is used. In many online communities, it is the norm to redact names and other sensitive text from posted screenshots. Hence, our findings highlight a subtle attack that must be considered when de- classifying images. Although the requirements are stringent, it will not be surprising that redacted images matching the requirements can be found in the public domain. We found that, if a redacted image is compressed in higher bit rate compared to the compression of the original image, then the cor- rect template can be identified with noticeable certainty. We give two approaches and investigate their effectiveness when the image is compressed using JPEG or wavelet-based compression scheme. We consider a sce- nario where the goal of the adversary is to identify the original among,a few templates.
Although such residual information is insufficient to reconstruct theoriginal, it can be exploited when,the content has low entropy. Hence, information of the original pixels might not be com- pletely purged by replacing pixels in the compressed image. Since digital images are usually lossily compressed via quantization in the frequency domain, each pixel in the spatial domain will be “spread” to its surroundings, similar to the Gibbs-effect, before it is redacted. Our goal is to study the effectiveness of this simple method,in purg- ing information. A common,way,to remove,the sensitive information replaces the pixels in the sensitive region with black or white values. Many digital images need to be redacted before they can be disseminated.
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