Forensic research on detecting seam carving in digital images

Digital images have been playing an important role in our daily life for the last several decades. Naturally, image editing technologies have been tremendously developed due to the increasing demands. As a result, digital images can be easily manipulated on a personal computer or even a cellphone for many purposes nowadays, so that the authenticity of digital images becomes an important issue. In this dissertation research, four machine learning based forensic methods are presented to detect one of the popular image editing techniques, called ‘seam carving’. To reveal seam carving applied to uncompressed images from the perspective of energy distribution change, an energy based statistical model is proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A support vector machine (SVM) based classifier is employed to determine whether an image has been seam carved or not. In the second work, an advanced feature model is presented for seam carving detection by investigating the statistical variation among neighboring pixels. Comprised with three types of statistical features, i.e., LDP features, Markov features, and SPAM features, the powerful feature model significantly improved the state-of-the-art accuracy in detecting low carving rate seam carving. After the feature selection by utilizing SVM based recursive feature elimination (SVM-RFE), with a small amount of features selected from the proposed model the overall performance is further improved. Combining above mentioned two works, a hybrid feature model is then proposed as the third work to further boost the accuracy in detecting seam carving at low carving rate. The proposed model consists of two sets of features, which capture energy change and neighboring relationship variation respectively, achieves remarkable performance on revealing seam carving, especially low carving rate seam carving, in digital images. Besides these three hand crafted feature models, a deep convolutional neural network is designed for seam carving detection. It is the first work that successfully utilizes deep learning technology to solve this forensic problem. The experimental works demonstrate their much more improved performance in the cases where the amount of seam carving is not serious. Although these four pieces of work move the seam carving detection ahead substantially, future research works with more advanced statistical model or deep neural network along this line are expected

Image Resizing using Seam Carving

Image resizing has become more necessary with the increased popularity of cell phones, tablets and other electronic devices with varying screen sizes. This paper presents methods for resizing images and videos while attempting to preserve the important content of that image or video. An algorithm called seam carving can expand or reduce the size of an image while typically maintaining quality and content. Seam carving is not always effective however and there have been recent developments and modifications on this algorithm. This paper presents two advancements on seam carving, one that optimizes image retargeting on images with many repeated objects or patterns. The other applies the method of seam carving to video resizing

Implementasi Content Aware Pada Pembuatan Thumbnail Menggunakan Metode Seam Carving and Salient Detection

Image resizing merupakan suatu proses pengelolahan citra yang bertujuan mengubah ukuran gambar ke ukuran yang diinginkan oleh pengguna. Metode yang paling sering digunakan untuk mengubah ukuran gambar pada pembuatan thumbnail adalah scaling dan cropping. Scaling  merupakan pengubahan ukuran citra berdasarkan skala tanpa mempertimbangkan proporsi panjang dan lebarnya. dan juga tidak mempertimbangkan isi dari citra. Cropping terbatas karena hanya menghilangkan pixel pada citra dalam batasan area tertentu saja. Akibatnya, dalam thumbnail yang dihasilkan tidak dapat menyampaikan informasi yang penting pada gambar. Seam carving merupakan salah satu metode untuk mengubah ukuran gambar dengan menghapus atau menambahkan ukiran (carve) piksel-piksel dari bagian-bagian gambar yang berbeda sesuai konten (content-aware). Seam carving menawarkan kelebihan dibanding scaling dan cropping.. Namun, metode seam carving masih gagal untuk melindungi objek penting pada gambar. Untuk itu dalam mengatasi kelemahan tersebut, dalam penelitian ini akan dilakukan implementasi Seam carving dan metode salient detection yang digunakan untuk pembuatan thumbnail. Hasil salient detection mendeteksi daerah terpenting dari gambar dan sebagai acuan dalam mengubah ukuran gambar (seam carving)

Seam Cerving and Salient Detection for Thumbnail Photos

Image resizing is a process of processing images or images with the aim of changing the size of the image. The most commonly used methods are cropping or scaling. Scaling is changing the size of the image based on the scale. Contents in the image are not considered in scaling. Seam carving often uses energy functionality that is useful as a determinant of the pixel level contained in an image. Seam is a connecting path of image pixels both vertically and horizontally that is passed by a low energy function. Changing the image size using seam carving is considered better than cropping and scaling. However, the seam carving method still cannot protect the object that is considered the most important. In overcoming this weakness, we can use a combination of seam carving algorithm with salient detection. In this research, we will improve the two methods which function as thumbnail maker. The results of the salient detection of the most important areas of the image will be detected and as a reference in resizing the image (seam carving) The dataset uses 200 images. The accuracy value is calculated by distributing questionnaires to 100 respondents and producing an acceptance rate of 78% so that the results are Very Natural/Natural

Implementasi Deteksi Seam Carving Berdasarkan Perubahan Ukuran Citra Menggunakan Local Binary Patterns dan Support Vector Machine

Seam carving adalah metode yang digunakan untuk content-aware image resizing. Seam carving bertujuan untuk mengubah ukuran citra atau image resizing dengan tidak menghilangkan konten penting yang ada pada citra. Dalam bidang forensik digital, seam carving banyak dibahas khususnya tentang deteksi seam carving pada citra. Hal tersebut bertujuan untuk mengetahui apakah suatu citra sudah pernah melalui proses pengubahan ukuran menggunakan seam carving atau belum.Tugas akhir ini mengusulkan sebuah metode deteksi seam carving berdasarkan perubahan ukuran citra menggunakan Local Binary Patterns dan Support Vector Machine. Citra yang akan dideteksi dihitung variasi teksturnya menggunakan Local Binary Patterns. Proses selanjutnya adalah ekstraksi fitur dari distribusi energy yang menghasilkan 24 fitur. Data fitur citra selanjutnya dilakukan proses normalisasi. Uji coba fitur menggunakan k-fold cross validation dengan membagi data menjadi training dan testing. Selanjutnya data tersebut akan memasuki proses klasifikasi menggunakan Support Vector Machine dengan kernel Radial Basis Function.Uji coba dilakukan terhadap citra asli dan citra seam carving. Citra seam carving yang digunakan dibedakanviiiberdasarkan skala rasionya yaitu 10%, 20%, 30%, 40%, dan 50%. Jumlah data yang digunakan adalah sebanyak 400 citra untuk setiap uji coba pada tiap skala rasio dengan menggunakan 10-fold cross validation. Rata-rata akurasi terbaik yang dihasilkan sebesar 73,95%

Image resizing using saliency strength map and seam carving for white blood cell analysis

<p>Abstract</p> <p>Background</p> <p>A new image-resizing method using seam carving and a Saliency Strength Map (SSM) is proposed to preserve important contents, such as white blood cells included in blood cell images.</p> <p>Methods</p> <p>To apply seam carving to cell images, a SSM is initially generated using a visual attention model and the structural properties of white blood cells are then used to create an energy map for seam carving. As a result, the energy map maximizes the energies of the white blood cells, while minimizing the energies of the red blood cells and background. Thus, the use of a SSM allows the proposed method to reduce the image size efficiently, while preserving the important white blood cells.</p> <p>Results</p> <p>Experimental results using the PSNR (Peak Signal-to-Noise Ratio) and ROD (Ratio of Distortion) of blood cell images confirm that the proposed method is able to produce better resizing results than conventional methods, as the seam carving is performed based on an SSM and energy map.</p> <p>Conclusions</p> <p>For further improvement, a faster medical image resizing method is currently being investigated to reduce the computation time, while maintaining the same image quality.</p

Implementasi Deteksi Seam Carving Berdasarkan Perubahan Ukuran Citra Menggunakan Local Binary Patterns dan Support Vector Machine

Seam carving adalah metode yang digunakan untuk content-aware image resizing. Seam carving bertujuan untuk mengubah ukuran citra atau image resizing dengan tidak menghilangkan konten penting yang ada pada citra. Dalam bidang forensik digital, seam carving banyak dibahas khususnya tentang deteksi seam carving pada citra. Hal tersebut bertujuan untuk mengetahui apakah suatu citra sudah pernah melalui proses pengubahan ukuran menggunakan seam carving atau belum. Tugas akhir ini mengusulkan sebuah metode deteksi seam carving berdasarkan perubahan ukuran citra menggunakan Local Binary Patterns dan Support Vector Machine. Citra yang akan dideteksi dihitung variasi teksturnya menggunakan Local Binary Patterns. Proses selanjutnya adalah ekstraksi fitur dari distribusi energy yang menghasilkan 24 fitur. Data fitur citra selanjutnya dilakukan proses normalisasi. Uji coba fitur menggunakan k-fold cross validation dengan membagi data menjadi training dan testing. Selanjutnya data tersebut akan memasuki proses klasifikasi menggunakan Support Vector Machine dengan kernel Radial Basis Function. Uji coba dilakukan terhadap citra asli dan citra seam carving. Citra seam carving yang digunakan dibedakan viii berdasarkan skala rasionya yaitu 10%, 20%, 30%, 40%, dan 50%. Jumlah data yang digunakan adalah sebanyak 400 citra untuk setiap uji coba pada tiap skala rasio dengan menggunakan 10-fold cross validation. Rata-rata akurasi terbaik yang dihasilkan sebesar 73,95%. ======================================================================================================================== Seam carving is method used for content-aware image resizing. Seam carving is designed to resize the image by not eliminating the important content that is in the image. In digital forensic area, seam carving is much discussed, especially about seam carving detection in image. It aims to determine whether an image has been through the process of resizing using seam carving or not. This final project propose a method of seam carving detection based on image resizing using Local Binary Patterns and Support Vector Machine. The texture variation of image will be calculated using Local Binary Patterns. The next process is extraction process from energy distribution and produces 24 features. The feature data is then performed normalization process. The normalized features will be tested using k-fold cross validation by dividing the data into training and testing. Finally, the data will be classified using Support Vector Machine with Radial Basis Function kernel. The trial test use original image and seam carved image. Seam carved image used is differentiated by its scaling ratios of 10%, 20%, 30%, 40%, and 50%. There are 400 image used for each trial test on each scaling ratios by using 10-fold cross validation. The best average accuracy is 73,95%