Secure and Robust Image Watermarking Scheme Using Homomorphic Transform, SVD and Arnold Transform in RDWT Domain

The main objective for a watermarking technique is to attain imperceptibility, robustness and security against various malicious attacks applied by illicit users. To fulfil these basic requirements for a scheme is a big issue of concern. So, in this paper, a new image watermarking method is proposed which utilizes properties of homomorphic transform, Redundant Discrete Wavelet Transform (RDWT), Arnold Transform (AT) along with Singular Value Decomposition (SVD) to attain these required properties. RDWT is performed on host image to achieve LL subband. This LL subband image is further decomposed into illumination and reflectance components by homomorphic transform. In order to strengthen security of proposed scheme, AT is used to scramble watermark. This scrambled watermark is embedded with Singular Values (SVs) of reflectance component which are obtained by applying SVD to it. Since reflectance component contains important features of image, therefore, embedding of watermark in this part provides excellent imperceptibility. Proposed scheme is comprehensively examined against different attacks like scaling, shearing etc. for its robustness. Comparative study with other prevailing algorithms clearly reveals superiority of proposed scheme in terms of robustness and imperceptibility

Human-machine cooperation in large-scale multimedia retrieval : a survey

Large-Scale Multimedia Retrieval(LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more interdisciplinary approach is necessary to develop an LSMR system that is really meaningful for humans. To this end, this paper aims to stimulate attention to the LSMR problem from diverse research fields. By explaining basic terminologies in LSMR, we first survey several representative methods in chronological order. This reveals that due to prioritizing the generality and scalability for large-scale data, recent methods interpret semantic meanings with a completely different mechanism from humans, though such humanlike mechanisms were used in classical heuristic-based methods. Based on this, we discuss human-machine cooperation, which incorporates knowledge about human interpretation into LSMR without sacrificing the generality and scalability. In particular, we present three approaches to human-machine cooperation (cognitive, ontological, and adaptive), which are attributed to cognitive science, ontology engineering, and metacognition, respectively. We hope that this paper will create a bridge to enable researchers in different fields to communicate about the LSMR problem and lead to a ground-breaking next generation of LSMR systems

Human-Machine Cooperation in Large-Scale Multimedia Retrieval: A Survey

Large-Scale Multimedia Retrieval(LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more interdisciplinary approach is necessary to develop an LSMR system that is really meaningful for humans. To this end, this paper aims to stimulate attention to the LSMR problem from diverse research fields. By explaining basic terminologies in LSMR, we first survey several representative methods in chronological order. This reveals that due to prioritizing the generality and scalability for large-scale data, recent methods interpret semantic meanings with a completely different mechanism from humans, though such humanlike mechanisms were used in classical heuristic-based methods. Based on this, we discuss human-machine cooperation, which incorporates knowledge about human interpretation into LSMR without sacrificing the generality and scalability. In particular, we present three approaches to human-machine cooperation (cognitive, ontological, and adaptive), which are attributed to cognitive science, ontology engineering, and metacognition, respectively. We hope that this paper will create a bridge to enable researchers in different fields to communicate about the LSMR problem and lead to a ground-breaking next generation of LSMR systems

Analisis Interaksi Pengguna di Media Sosial Dalam Mencegah Video Hoax dan Model Arsitektur Deteksi Tingkat Tinggi

Penyebaran berita hoaks dengan konten video yang berulang pada media sosial merupakan fenomenayang sangat luar biasa dan muncul bukan hanya pada kalangan pengguna dewasa saja namun sudah kesegalalapisan usia, Efek yang paling terasa adalah timbulnya perpecahan di masyarakat karena penggunaan videoyang sudah pernah tayang atau ada sebelumnya menjadi bukti kuat untuk memvalidasi konten yang dilihatnya.Penting untuk mendeteksi berita hoaks dengan konten video yang berulang dan menghentikan efek negatifnyapada individu dan masyarakat. Pada penelitian ini pembuatan model arsitektur deteksi tingkat tinggi untuksistem analisis berita hoaks dengan konten video yang digunakan kembali atau berulang pada media sosial dikenalkan, dengan menggunakan deep learning video processing, speech to text dan beberapa fitur content-baseddan context-based rancangan model arsitektur ini dibuat. Konten hoaks dengan video yang berulang diharapkandapat dicegah penyebarannya jika bisa di filter terlebih dahulu sebelum muncul di lini masa. Diharapkan modelarsitektur ini dapat menjadi referensi untuk di buat menjadi real syste