Communication channel established from a display to a device's camera is known as visual channel, and it is helpful in securing key exchange protocol. In this paper, we study how visual channel can be exploited by a network terminal and mobile device to jointly verify information in an interactive session, and how such information can be jointly presented in a user-friendly manner, taking into account that the mobile device can only capture and display a small region, and the user may only want to authenticate selective regions-of-interests. Motivated by applications in Kiosk computing and multi-factor authentication, we consider three security models: (1) the mobile device is trusted, (2) at most one of the terminal or the mobile device is dishonest, and (3) both the terminal and device are dishonest but they do not collude or communicate. We give two protocols and investigate them under the abovementioned models. We point out a form of replay attack that renders some other straightforward implementations cumbersome to use. To enhance user-friendliness, we propose a solution using visual cues embedded into the 2D barcodes and incorporate the framework of "augmented reality" for easy verifications through visual inspection. We give a proof-of-concept implementation to show that our scheme is feasible in practice.
Data can be transmitted to a camera effectively using 2D barcodes. There are many 2D barcode designs, for example, QR code @cite_10 and the High Capacity Color Barcode (HCCB) @cite_4 that uses colored triangles. Many barcodes are designed to encode data in printed copies. There are also proposals that use other types of sources in the visual channel. proposed Screen codes'' @cite_21 for transferring data from a display to a camera-equipped mobile device, where the data are encoded as a grid of luminosity fluctuation within an arbitrary image. A challenging hurdle in using hand-held cameras to establish the channel is motion blur. A few stabilization algorithms are developed for handheld camera @cite_23 @cite_14 , and for 2D barcodes @cite_6 .
@cite_14 @cite_4 @cite_21 @cite_6 @cite_23 @cite_10
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Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications. For instance, they can enable users to access reviews or price comparisons for a product from a picture of its barcode while still in the store. Barcode reading needs to be robust to challenging conditions such as blur, noise, low resolution, or low-quality camera lenses, all of which are extremely common. Surprisingly, even state-of-the-art barcode reading algorithms fail when some of these factors come into play. One reason resides in the early commitment strategy that virtually all existing algorithms adopt: The image is first binarized and then only the binary data are processed. We propose a new approach to barcode decoding that bypasses binarization. Our technique relies on deformable templates and exploits all of the gray-level information of each pixel. Due to our parameterization of these templates, we can efficiently perform maximum likelihood estimation independently on each digit and enforce spatial coherence in a subsequent step. We show by way of experiments on challenging UPC-A barcode images from five different databases that our approach outperforms competing algorithms. Implemented on a Nokia N95 phone, our algorithm can localize and decode a barcode on a VGA image (640 × 480, JPEG compressed) in an average time of 400-500 ms. With the proliferation of built-in cameras barcode scanning on smartphones has become widespread in both consumer and enterprise domains. To avoid making the user precisely align the barcode at a dedicated position and angle in the camera image, barcode localization algorithms are necessary that quickly scan the image for possible barcode locations and pass those to the actual barcode decoder. In this paper, we present a barcode localization approach that is orientation, scale, and symbology (1D and 2D) invariant and shows better blur invariance than existing approaches while it operates in real time on a smartphone. Previous approaches focused on selected aspects such as orientation invariance and speed for 1D codes or scale invariance for 2D codes. Our combined method relies on the structure matrix and the saturation from the HSV color system. The comparison with three other real-time barcode localization algorithms shows that our approach outperforms the state of the art with respect to symbology and blur invariance at the expense of a reduced speed. Visual codes are used to embed digital data in physical objects, or they are shown in video sequences to transfer data over screen camera links. Existing codes either carry limited data to make them robust against a range of channel conditions (e.g., low camera quality or long distances), or they support a high data capacity but only work over a narrow range of channel conditions. We present FOCUS, a new code design that does not require this explicit trade-off between code capacity and the reader's channel quality. Instead, FOCUS builds on concepts from OFDM to encode data at different levels of spatial detail. This enables each reader to decode as much data from a code as its channel quality allows. We build a prototype of FOCUS devices and evaluate it experimentally. Our results show that FOCUS gracefully adapts to the reader's channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet. A 2D color barcode can hold much more information than a binary barcode. Barcodes are often intended for consumer use where using a cellphone, a consumer can take an image of a barcode on a product, and retrieve relevant information about the product. The barcode must be read using computer vision techniques. While a color barcode can hold more information, it makes this vision task in consumer scenarios unusually challenging. We present our approach to the localization and segmentation of a 2D color barcode in such challenging scenarios, along with its evaluation on a diverse collection of images of Microsoft's recently launched high capacity color barcode (HCCB). We exploit the unique trait of barcode reading: the barcode decoder can give the vision algorithm feedback, and develop a progressive strategy to achieve both - high accuracy in diverse scenarios as well as computational efficiency.