Copyright Protection of Images Based on Large-Scale Image Recognition Koichi Kise, Satoshi Yokota, Akira Shiozaki Osaka Prefecture University Outline Demo Image Recognition with Local Descriptors Our Research Topics Copyright Protection and Digital Watermarking Proposed Method Experiments Conclusion 2 DEMO Large-Scale Image Recognition Outline Demo Image Recognition with Local Descriptors Our Research Topics Copyright Protection and Digital Watermarking Proposed Method Experiments Conclusion 4 Image Recognition with Local Descriptors Image 1 Image 2 Image 4 Typically a few hundreds to a few thousands are extracted Database Image 3 Images are represented by local descriptors (feature vectors) Query 5 Database Image 2 1 2 3 4 Image 4 Nearest vector in the DB Image 1 max. votes Image 3 no. of votes Image Recognition with Local DescriptorsNearest Neighbor Search Query 6 Pros and Cons Locality and stability of descriptors Large number of expensive descriptors CONS PROS Robust to occlusion and variations 7 Extraction of descriptors a few seconds / query Matching By brute-force NN search with the DB of 1,000 images 315 seconds / query Outline Demo Image Recognition with Local Descriptors Our Research Topics Copyright Protection and Digital Watermarking Proposed Method Experiments Conclusion 8 Our Research Topics Extraction of Descriptors Baseline Software Matching (with 10,000 images) 2.3 sec. Brute-force Existing Technologies 3,000 sec. ANN (Approximate Nearest Neighbor) 50 ms 60,000 times faster Our Methods GPU & CUDA 0.57 sec. Hash & Cascade 4 times faster Application Copyright Protection 9 0.78 ms 60 times faster Outline Demo Image Recognition with Local Descriptors Our Research Topics Copyright Protection and Digital Watermarking Proposed Method Experiments Conclusion 10 Copyright Protection and Digital Watermarking Digital Watermarking is a technology to embed information (copyright notice) to original digital data. Embedded information (watermark) is often unnoticeable. We focus on digital images as original data. Copyright: A Purchaser: B original Methods: 11 Non-blind Blind watermarked Non-Blind and Blind Watermarking Non-blind watermarking watermarked image & original image Blind watermarking watermarked image Copyright: A Purchaser: B Copyright: A Purchaser: B History From non-blind to blind watermarking 12 Non-blind watermarking needs matching of images spoils the scalability Blind watermarking has been the focus of interest Limitations of Blind Watermarking StirMark distorts images imperceptibly severely damages the wartermark Original 13 StirMark StirMark: Some Image Transformations cropping fraction of pixel displacement original punch / pinch 14 corner shift Outline Demo Image Recognition with Local Descriptors Our Research Topics Copyright Protection and Digital Watermarking Proposed Method Experiments Conclusion 15 Proposed Method: Ideas How do we solve the problem of blind watermarking? Cancel the effect of StirMark, especially geometric transformations normalization StirMark’ed Normalization could be done by comparing with the original image StirMark’ed 16 normalized StirMark’ed Blind to non-blind Original normalized StirMark’ed Proposed Method: Ideas How to solve the problem of non-blind watermarking? Matching problem solved by the efficient and robust image recognition How about the normalization? Image recognition with local descriptors 17 provides matching of descriptors as a side effect helpful for normalization Proposed Method: Processing Steps STEP1: Recognition STEP2: Normalization Image Database query image point matching STEP3: Detection Embedded Watermark 19 Proposed Method: Processing Steps STEP1: Recognition STEP2: Normalization Image Database query image point matching Local Descriptors: PCA-SIFT Matching: ANN 20 STEP3: Detection Embedded Watermark Outline Demo Image Recognition with Local Descriptors Our Research Topics Copyright Protection and Digital Watermarking Proposed Method Experiments Conclusion 21 Experiments: Overview Experiment 1 Experiment 2 Robustness against StirMark with default setting (imperceptible change) Robustness against perceptible changes Experiment 3 22 Robustness against watermarking Experiments: Overview Experiment 1 Experiment 2 Robustness against StirMark with default setting (imperceptible change) Robustness against perceptible changes Experiment 3 23 Robustness against watermarking Experiments: Dataset & Task Base set 10,000 images 100 Watermark StirMark query images Input : query image Task: find its original from 10,000 images 24 Experiment 1 10,000 with 10,000 images 100 Watermark without 10,000 images 100 StirMark imperceptible 100 random application with default setting StirMark watermarking without with accuracy 100% 99.49% time (per query) 217ms 216ms 25 query images query images Experiment2 4 image transformations cropping, fraction of pixel displacement, punch/pinch, corner shift perceptible 4,300 with 10,000 images 100 Watermark without 10,000 images 26 100 StirMark query images perceptible 4 trans. 43 application in total StirMark query images Cropping: Original Image Cropping: 70; Accuracy 97% 29 Cropping: 50; Accuracy: 95% 30 Cropping: 30; Accuracy: 92% 31 Accuracy [%] Cropping With watermarking Without watermarking Cropping parameter 32 Punch / Pinch Original Image 33 Punch / Pinch: 1; Accuracy: 99% 34 Punch / Pinch: 25; Accuracy: 99% 35 Punch / Pinch: 50; Accuracy: 97% 36 Punch / Pinch: 100; Accuracy: 50% 37 Accuracy [%] Punch / Pinch With watermarking Without watermarking Punch / Pinch Parameter 38 Conclusion A non-blind framework for copyright protection of images has been proposed Fundamental experiments on image recognition Efficient image recognition technology with local descriptors Image recognition is robust to StirMark attacks and watermarking Future Work 39 Completion of overall method Copyright Protection of Images Based on Large-Scale Image Recognition Thank you for your attention Image Recognition: Local Descriptors PCA-SIFT (PCA Scale-Invariant Feature Transform) 41 36 dimensions Invariant to scale and rotation changes Image Recognition: Matching ANN(Approximate Nearest Neighbor) Tree structure Feature Space Feature Vector ANN ：Feature Vector of a query ANN Nearest Neighbor Search for ANN Approximate NN Search for Result This cell is not searched ANN Approximate NN Search for Correct more approximation with a smaller circle Errors caused by approximation Wrong Image recognition rate[%] NNS accuracy vs. Image recognition rate 100 95 ANN dist E2LSH 90 85 80 0 10 20 30 40 50 60 70 80 90 100 NNS accuracy[%] 47 Corner Shift Original Image 48 Corner Shift: 1; Accuracy: 99% 49 Corner Shift: 100; Accuracy: 98% 50 Corner Shift: 150; Accuracy: 96% 51 Corner Shift: 225; Accuracy: 74% 52 Accuracy [%] Corner Shift With watermarking Without watermarking Corner Shift Parameter 53 Fraction of Pixel Displacement Original Image 54 Fraction of Pixel Displacement: 1; Accuracy 99% 55 Fraction of Pixel Displacement: 3 Accuracy: 94% 56 Fraction of Pixel Displacement: 5; Accuracy: 81% 57 Fraction of Pixel Displacement: 8 Accuracy: 58% 58 Fraction of Pixel Displacement: 10 Accuracy: 24% 59 Accuracy [%] Fraction of Pixel Displacement With watermarking Without watermarking Fraction Parameter 60
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