A New Approach of Cryptographic Technique Using Simple ECC ECF

Cryptography is the technique in which usually a file is converted into unreadable format by using public key and private key system called as public key cryptosystem. Then as per the user requirement that file is send to another user for secure data transmission. In this paper we purposed an image based cryptography that Elliptic Curve Function (ECF) techniques and pseudo random encoding technique on images to enhance the security of RFID communication. In the ECF approach, the basic idea is to replace the Elliptic Curve Function (ECF) of the cover image with the Bits of the messages to be hidden without destroying the property of the cover image significantly. The ECF based technique is the most challenging one as it is difficult to differentiate between the cover object and Crypto object if few ECF bits of the cover object are replaced. In Pseudo Random technique, a random key is used as seed for the Pseudo Random Number Generator in needed in the embedding process. Both the techniques used a Crypto key while embedding messages inside the cover image. By using the key, the chance of getting attacked by the attacker is reduced

From average case complexity to improper learning complexity

The basic problem in the PAC model of computational learning theory is to determine which hypothesis classes are efficiently learnable. There is presently a dearth of results showing hardness of learning problems. Moreover, the existing lower bounds fall short of the best known algorithms. The biggest challenge in proving complexity results is to establish hardness of {\em improper learning} (a.k.a. representation independent learning).The difficulty in proving lower bounds for improper learning is that the standard reductions from $\mathbf{NP}$-hard problems do not seem to apply in this context. There is essentially only one known approach to proving lower bounds on improper learning. It was initiated in (Kearns and Valiant 89) and relies on cryptographic assumptions. We introduce a new technique for proving hardness of improper learning, based on reductions from problems that are hard on average. We put forward a (fairly strong) generalization of Feige's assumption (Feige 02) about the complexity of refuting random constraint satisfaction problems. Combining this assumption with our new technique yields far reaching implications. In particular, 1. Learning $\mathrm{DNF}$'s is hard. 2. Agnostically learning halfspaces with a constant approximation ratio is hard. 3. Learning an intersection of $\omega(1)$ halfspaces is hard.Comment: 34 page

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields