User-Centric Key Entropy: Study of Biometric Key Derivation Subject to Spoofing Attacks
<p>Fingerprint minutiae extraction process. (<b>a</b>) Enhanced fingerprint; (<b>b</b>) Binarized fingerprint; (<b>c</b>) Thinned with minutiae; (<b>d</b>) Selected minutiae; (<b>e</b>) ROI; (<b>f</b>) Minutiae with orientation.</p> "> Figure 2
<p>Iris features extraction process. (<b>a</b>) Iris segmentation; (<b>b</b>) Iris normalised; (<b>c</b>) Iris normalised noise.</p> "> Figure 3
<p>Results for keys derived from fingerprint. (<b>a</b>) KEY length 110 bits—Fingerprint hamming distance SET 1 all tests; (<b>b</b>) KEY length 110 bits—Fingerprint hamming distance SET 2 all tests; (<b>c</b>) KEY length 322 bits—Fingerprint hamming distance SET 1 all tests; (<b>d</b>) KEY length 322 bits—Fingerprint hamming distance SET 2 all tests; (<b>e</b>) KEY length 496 bits—Fingerprint hamming distance SET 1 all tests; (<b>f</b>) KEY length 496 bits—Fingerprint hamming distance SET 2 all tests.</p> "> Figure 4
<p>Results for keys derived from iris. (<b>a</b>) KEY length 110 bits—Iris hamming distance SET 1 all tests; (<b>b</b>) KEY length 110 bits—Iris hamming distance SET 2 all tests; (<b>c</b>) KEY length 322 bits—Iris hamming distance SET 1 all tests; (<b>d</b>) KEY length 322 bits—Iris hamming distance SET 2 all tests; (<b>e</b>) KEY length 496 bits—Iris hamming distance SET 1 all tests; (<b>f</b>) KEY length 496 bits—Iris hamming distance SET 2 all tests.</p> "> Figure 5
<p>Comparative results for multi-biometric keys. (<b>a</b>) OFOI-OFFI all tests—Key 110 bits SET 1; (<b>b</b>) OFOI-OFFI all tests—Key 322 bits SET 1; (<b>c</b>) OFOI-OFFI all tests—Key 496 bits SET 1; (<b>d</b>) OFOI-FFOI all tests—Key 110 bits SET 1; (<b>e</b>) OFOI-FFOI all tests—Key 322 bits SET 1; (<b>f</b>) OFOI-FFOI all tests—Key 496 bits SET 1; (<b>g</b>) OFOI-FFFI all tests—Key 110 bits SET 1; (<b>h</b>) OFOI-FFFI all tests—Key 322 bits SET 1; (<b>i</b>) OFOI-FFFI all tests—Key 496 bits SET 1.</p> "> Figure 5 Cont.
<p>Comparative results for multi-biometric keys. (<b>a</b>) OFOI-OFFI all tests—Key 110 bits SET 1; (<b>b</b>) OFOI-OFFI all tests—Key 322 bits SET 1; (<b>c</b>) OFOI-OFFI all tests—Key 496 bits SET 1; (<b>d</b>) OFOI-FFOI all tests—Key 110 bits SET 1; (<b>e</b>) OFOI-FFOI all tests—Key 322 bits SET 1; (<b>f</b>) OFOI-FFOI all tests—Key 496 bits SET 1; (<b>g</b>) OFOI-FFFI all tests—Key 110 bits SET 1; (<b>h</b>) OFOI-FFFI all tests—Key 322 bits SET 1; (<b>i</b>) OFOI-FFFI all tests—Key 496 bits SET 1.</p> "> Figure 6
<p>Biometric attack places, as defined by [<a href="#B55-entropy-19-00070" class="html-bibr">55</a>].</p> ">
Abstract
:1. Introduction
2. Literature Review
3. System Implementation Details
3.1. Fingerprint Feature Extraction
- Image pre-processing, depicted in Figure 1a. Image enhancement is very important because the quality of the extraction process is dependant on the enhancement algorithm. Image enhancement is done by applying Gabor Filter to the image. Another important step in the preprocessing stage is limiting the search of the fingerprint extraction by only detecting and storing the position and orientation of bifurcations and terminations.
- Image binarization, depicted in Figure 1b. The purpose of this process is to differentiate between ridges marked with black and furrows marked with white.
- Image thinning and minutiae extraction, depicted in Figure 1c. The image is thinned so ridges are only one pixel wide, and all the redundant pixel information is eliminated. For minutiae extraction, the local neighbourhood of each pixel ridge is scanned using a 3 × 3 window. A pixel is either a termination (central pixel is 1 and 1 value neighbour), bifurcation (central pixel is 1 and 3 value neighbour), and usual pixel (central pixel is 1 and 2 value neighbour). The Terminations are marked with red and bifurcations with green.
- Minutiae processing, depicted in Figure 1d. This step eliminates minutiae that are too close together. If the distance between a termination and a bifurcation is smaller then a minimum accepted value d, then that minutiae is removed.
- ROI, depicted in Figure 1e. A Region of Interest is defined and based on that region the extra minutiae are omitted.
- Orientation, depicted in Figure 1f. When the minutiae extraction is complete, the orientation of each element, bifurcation, termination and their respective angles.
- Binary template. The extracted points are each binarised resulting in a binary biometric template.
3.2. Iris Feature Extraction
- Segmentation. Is the most important step and the success of this step determines the quality of the feature extraction. Image segmentation translates into isolating the iris image from the input image. Circular Hough transform is used to detect the boundaries of iris and pupil. Depending on the database quality specular reflections should be removed. The process is depicted in Figure 2a.
- Normalisation. This process creates fixed iris dimensions to allow comparisons, because no two iris images are alike due to different environmental factors when capturing the image, like: distance from the sensor, amount of light. Figure 2b,c depict normalised iris and noise corresponding to normalised image.
- Feature encoding. Feature encoding is done using 1D Log-Gabor wavelets, creating a iris bitwise template and a corresponding noise mask.
3.3. Extraction oF Biometric Key
Listing 1: Partial listing for RSA key generation. public void generateKey(String features, int size, String fileName, String path) { try { byte[] featuresBytes = features.getBytes(); SecureRandom random = SecureRandom.getInstance("SHA1PRNG"); random.setSeed(featuresBytes); KeyPairGenerator keyGen = KeyPairGenerator.getInstance("RSA"); keyGen.initialize(size, random); KeyPair generatedKeyPair = keyGen.genKeyPair(); saveEncodedKeyPair(generatedKeyPair, fileName, size, path); } catch (Exception e) { e.printStackTrace(); return; } }
4. Experiments and Results
4.1. Fingerprints
4.2. Iris
4.3. Multi-Biometrics
- OFOI-OFFI Original samples against original fingerprint and fake iris sample.
- OFOI-FFOI Original samples against fake fingerprint and original iris sample.
- OFOI-FFFI Original samples against fake samples for both biometrics.
5. Security Analysis
5.1. Overall Biometric System Security
- (a) Biometric spoofing—the biometric characteristic is spoofed and presented to the sensor.
- (b) Relaying stored signals—a recorded signal is relayed to the system, overwriting the captured biometric data.
- (c) Attacking feature extraction—the feature extraction process itself is under attack, the intruder changes the extracted features with the desired set.
- (d) Tampering with biometric features—the extracted features are intercepted before going to the matcher module and replaced with the desired set.
- (e) Attacking the matcher—the matcher itself is under attack, meaning the intruder obtains the desired match scores.
- (f) Attacking the stored templates—penetrating the biometric database might allow an attacker to reconstruct the original biometric based on the template, or even worse replace a certain template with another one.
- (g) Communication interception—the communication between the database and the matcher might be intercepted by an intruder.
- (h) Attacking the final decision—an attacker will obtain the desired decision.
5.2. Smartphone Application Security
5.3. Liveness Detection
5.4. Which Biometrics to Use
5.5. Which Fusion Method to Use
5.6. No Access to Private Key
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
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Type | Name | Description |
---|---|---|
Cancelable biometrics | Biometric salting | Adds a salt (randoms bits) to the key. If the biometric template is compromised it can be renewed and the user doesn’t loose the biometric characteristic. |
Non-invertible transforms | The biometric data is transformed using a non-invertible function before being stored in the database. A template can be renewed by using a different transformation. | |
Biometric Cryptosystems | Key-binding schemes | The biometric template is binded by an encryption key and stored in the database. |
Key-generation schemes | The key is derived from the biometric template. |
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Dinca, L.M.; Hancke, G. User-Centric Key Entropy: Study of Biometric Key Derivation Subject to Spoofing Attacks. Entropy 2017, 19, 70. https://doi.org/10.3390/e19020070
Dinca LM, Hancke G. User-Centric Key Entropy: Study of Biometric Key Derivation Subject to Spoofing Attacks. Entropy. 2017; 19(2):70. https://doi.org/10.3390/e19020070
Chicago/Turabian StyleDinca, Lavinia Mihaela, and Gerhard Hancke. 2017. "User-Centric Key Entropy: Study of Biometric Key Derivation Subject to Spoofing Attacks" Entropy 19, no. 2: 70. https://doi.org/10.3390/e19020070